Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.
7 Module 7: Thinking, Reasoning, and Problem-Solving
This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure out the solution to many problems, because you feel capable of using logic to argue a point, because you can evaluate whether the things you read and hear make sense—you do not need any special training in thinking. But this, of course, is one of the key barriers to helping people think better. If you do not believe that there is anything wrong, why try to fix it?
The human brain is indeed a remarkable thinking machine, capable of amazing, complex, creative, logical thoughts. Why, then, are we telling you that you need to learn how to think? Mainly because one major lesson from cognitive psychology is that these capabilities of the human brain are relatively infrequently realized. Many psychologists believe that people are essentially “cognitive misers.” It is not that we are lazy, but that we have a tendency to expend the least amount of mental effort necessary. Although you may not realize it, it actually takes a great deal of energy to think. Careful, deliberative reasoning and critical thinking are very difficult. Because we seem to be successful without going to the trouble of using these skills well, it feels unnecessary to develop them. As you shall see, however, there are many pitfalls in the cognitive processes described in this module. When people do not devote extra effort to learning and improving reasoning, problem solving, and critical thinking skills, they make many errors.
As is true for memory, if you develop the cognitive skills presented in this module, you will be more successful in school. It is important that you realize, however, that these skills will help you far beyond school, even more so than a good memory will. Although it is somewhat useful to have a good memory, ten years from now no potential employer will care how many questions you got right on multiple choice exams during college. All of them will, however, recognize whether you are a logical, analytical, critical thinker. With these thinking skills, you will be an effective, persuasive communicator and an excellent problem solver.
The module begins by describing different kinds of thought and knowledge, especially conceptual knowledge and critical thinking. An understanding of these differences will be valuable as you progress through school and encounter different assignments that require you to tap into different kinds of knowledge. The second section covers deductive and inductive reasoning, which are processes we use to construct and evaluate strong arguments. They are essential skills to have whenever you are trying to persuade someone (including yourself) of some point, or to respond to someone’s efforts to persuade you. The module ends with a section about problem solving. A solid understanding of the key processes involved in problem solving will help you to handle many daily challenges.
7.1. Different kinds of thought
7.2. Reasoning and Judgment
7.3. Problem Solving
READING WITH PURPOSE
Remember and understand.
By reading and studying Module 7, you should be able to remember and describe:
- Concepts and inferences (7.1)
- Procedural knowledge (7.1)
- Metacognition (7.1)
- Characteristics of critical thinking: skepticism; identify biases, distortions, omissions, and assumptions; reasoning and problem solving skills (7.1)
- Reasoning: deductive reasoning, deductively valid argument, inductive reasoning, inductively strong argument, availability heuristic, representativeness heuristic (7.2)
- Fixation: functional fixedness, mental set (7.3)
- Algorithms, heuristics, and the role of confirmation bias (7.3)
- Effective problem solving sequence (7.3)
By reading and thinking about how the concepts in Module 6 apply to real life, you should be able to:
- Identify which type of knowledge a piece of information is (7.1)
- Recognize examples of deductive and inductive reasoning (7.2)
- Recognize judgments that have probably been influenced by the availability heuristic (7.2)
- Recognize examples of problem solving heuristics and algorithms (7.3)
Analyze, Evaluate, and Create
By reading and thinking about Module 6, participating in classroom activities, and completing out-of-class assignments, you should be able to:
- Use the principles of critical thinking to evaluate information (7.1)
- Explain whether examples of reasoning arguments are deductively valid or inductively strong (7.2)
- Outline how you could try to solve a problem from your life using the effective problem solving sequence (7.3)
7.1. Different kinds of thought and knowledge
- Take a few minutes to write down everything that you know about dogs.
- Do you believe that:
- Psychic ability exists?
- Hypnosis is an altered state of consciousness?
- Magnet therapy is effective for relieving pain?
- Aerobic exercise is an effective treatment for depression?
- UFO’s from outer space have visited earth?
On what do you base your belief or disbelief for the questions above?
Of course, we all know what is meant by the words think and knowledge . You probably also realize that they are not unitary concepts; there are different kinds of thought and knowledge. In this section, let us look at some of these differences. If you are familiar with these different kinds of thought and pay attention to them in your classes, it will help you to focus on the right goals, learn more effectively, and succeed in school. Different assignments and requirements in school call on you to use different kinds of knowledge or thought, so it will be very helpful for you to learn to recognize them (Anderson, et al. 2001).
Factual and conceptual knowledge
Module 5 introduced the idea of declarative memory, which is composed of facts and episodes. If you have ever played a trivia game or watched Jeopardy on TV, you realize that the human brain is able to hold an extraordinary number of facts. Likewise, you realize that each of us has an enormous store of episodes, essentially facts about events that happened in our own lives. It may be difficult to keep that in mind when we are struggling to retrieve one of those facts while taking an exam, however. Part of the problem is that, in contradiction to the advice from Module 5, many students continue to try to memorize course material as a series of unrelated facts (picture a history student simply trying to memorize history as a set of unrelated dates without any coherent story tying them together). Facts in the real world are not random and unorganized, however. It is the way that they are organized that constitutes a second key kind of knowledge, conceptual.
Concepts are nothing more than our mental representations of categories of things in the world. For example, think about dogs. When you do this, you might remember specific facts about dogs, such as they have fur and they bark. You may also recall dogs that you have encountered and picture them in your mind. All of this information (and more) makes up your concept of dog. You can have concepts of simple categories (e.g., triangle), complex categories (e.g., small dogs that sleep all day, eat out of the garbage, and bark at leaves), kinds of people (e.g., psychology professors), events (e.g., birthday parties), and abstract ideas (e.g., justice). Gregory Murphy (2002) refers to concepts as the “glue that holds our mental life together” (p. 1). Very simply, summarizing the world by using concepts is one of the most important cognitive tasks that we do. Our conceptual knowledge is our knowledge about the world. Individual concepts are related to each other to form a rich interconnected network of knowledge. For example, think about how the following concepts might be related to each other: dog, pet, play, Frisbee, chew toy, shoe. Or, of more obvious use to you now, how these concepts are related: working memory, long-term memory, declarative memory, procedural memory, and rehearsal? Because our minds have a natural tendency to organize information conceptually, when students try to remember course material as isolated facts, they are working against their strengths.
One last important point about concepts is that they allow you to instantly know a great deal of information about something. For example, if someone hands you a small red object and says, “here is an apple,” they do not have to tell you, “it is something you can eat.” You already know that you can eat it because it is true by virtue of the fact that the object is an apple; this is called drawing an inference , assuming that something is true on the basis of your previous knowledge (for example, of category membership or of how the world works) or logical reasoning.
Procedural knowledge
Physical skills, such as tying your shoes, doing a cartwheel, and driving a car (or doing all three at the same time, but don’t try this at home) are certainly a kind of knowledge. They are procedural knowledge, the same idea as procedural memory that you saw in Module 5. Mental skills, such as reading, debating, and planning a psychology experiment, are procedural knowledge, as well. In short, procedural knowledge is the knowledge how to do something (Cohen & Eichenbaum, 1993).
Metacognitive knowledge
Floyd used to think that he had a great memory. Now, he has a better memory. Why? Because he finally realized that his memory was not as great as he once thought it was. Because Floyd eventually learned that he often forgets where he put things, he finally developed the habit of putting things in the same place. (Unfortunately, he did not learn this lesson before losing at least 5 watches and a wedding ring.) Because he finally realized that he often forgets to do things, he finally started using the To Do list app on his phone. And so on. Floyd’s insights about the real limitations of his memory have allowed him to remember things that he used to forget.
All of us have knowledge about the way our own minds work. You may know that you have a good memory for people’s names and a poor memory for math formulas. Someone else might realize that they have difficulty remembering to do things, like stopping at the store on the way home. Others still know that they tend to overlook details. This knowledge about our own thinking is actually quite important; it is called metacognitive knowledge, or metacognition . Like other kinds of thinking skills, it is subject to error. For example, in unpublished research, one of the authors surveyed about 120 General Psychology students on the first day of the term. Among other questions, the students were asked them to predict their grade in the class and report their current Grade Point Average. Two-thirds of the students predicted that their grade in the course would be higher than their GPA. (The reality is that at our college, students tend to earn lower grades in psychology than their overall GPA.) Another example: Students routinely report that they thought they had done well on an exam, only to discover, to their dismay, that they were wrong (more on that important problem in a moment). Both errors reveal a breakdown in metacognition.
The Dunning-Kruger Effect
In general, most college students probably do not study enough. For example, using data from the National Survey of Student Engagement, Fosnacht, McCormack, and Lerma (2018) reported that first-year students at 4-year colleges in the U.S. averaged less than 14 hours per week preparing for classes. The typical suggestion is that you should spend two hours outside of class for every hour in class, or 24 – 30 hours per week for a full-time student. Clearly, students in general are nowhere near that recommended mark. Many observers, including some faculty, believe that this shortfall is a result of students being too busy or lazy. Now, it may be true that many students are too busy, with work and family obligations, for example. Others, are not particularly motivated in school, and therefore might correctly be labeled lazy. A third possible explanation, however, is that some students might not think they need to spend this much time. And this is a matter of metacognition. Consider the scenario that we mentioned above, students thinking they had done well on an exam only to discover that they did not. Justin Kruger and David Dunning examined scenarios very much like this in 1999. Kruger and Dunning gave research participants tests measuring humor, logic, and grammar. Then, they asked the participants to assess their own abilities and test performance in these areas. They found that participants in general tended to overestimate their abilities, already a problem with metacognition. Importantly, the participants who scored the lowest overestimated their abilities the most. Specifically, students who scored in the bottom quarter (averaging in the 12th percentile) thought they had scored in the 62nd percentile. This has become known as the Dunning-Kruger effect . Many individual faculty members have replicated these results with their own student on their course exams, including the authors of this book. Think about it. Some students who just took an exam and performed poorly believe that they did well before seeing their score. It seems very likely that these are the very same students who stopped studying the night before because they thought they were “done.” Quite simply, it is not just that they did not know the material. They did not know that they did not know the material. That is poor metacognition.
In order to develop good metacognitive skills, you should continually monitor your thinking and seek frequent feedback on the accuracy of your thinking (Medina, Castleberry, & Persky 2017). For example, in classes get in the habit of predicting your exam grades. As soon as possible after taking an exam, try to find out which questions you missed and try to figure out why. If you do this soon enough, you may be able to recall the way it felt when you originally answered the question. Did you feel confident that you had answered the question correctly? Then you have just discovered an opportunity to improve your metacognition. Be on the lookout for that feeling and respond with caution.
concept : a mental representation of a category of things in the world
Dunning-Kruger effect : individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do
inference : an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning
metacognition : knowledge about one’s own cognitive processes; thinking about your thinking
Critical thinking
One particular kind of knowledge or thinking skill that is related to metacognition is critical thinking (Chew, 2020). You may have noticed that critical thinking is an objective in many college courses, and thus it could be a legitimate topic to cover in nearly any college course. It is particularly appropriate in psychology, however. As the science of (behavior and) mental processes, psychology is obviously well suited to be the discipline through which you should be introduced to this important way of thinking.
More importantly, there is a particular need to use critical thinking in psychology. We are all, in a way, experts in human behavior and mental processes, having engaged in them literally since birth. Thus, perhaps more than in any other class, students typically approach psychology with very clear ideas and opinions about its subject matter. That is, students already “know” a lot about psychology. The problem is, “it ain’t so much the things we don’t know that get us into trouble. It’s the things we know that just ain’t so” (Ward, quoted in Gilovich 1991). Indeed, many of students’ preconceptions about psychology are just plain wrong. Randolph Smith (2002) wrote a book about critical thinking in psychology called Challenging Your Preconceptions, highlighting this fact. On the other hand, many of students’ preconceptions about psychology are just plain right! But wait, how do you know which of your preconceptions are right and which are wrong? And when you come across a research finding or theory in this class that contradicts your preconceptions, what will you do? Will you stick to your original idea, discounting the information from the class? Will you immediately change your mind? Critical thinking can help us sort through this confusing mess.
But what is critical thinking? The goal of critical thinking is simple to state (but extraordinarily difficult to achieve): it is to be right, to draw the correct conclusions, to believe in things that are true and to disbelieve things that are false. We will provide two definitions of critical thinking (or, if you like, one large definition with two distinct parts). First, a more conceptual one: Critical thinking is thinking like a scientist in your everyday life (Schmaltz, Jansen, & Wenckowski, 2017). Our second definition is more operational; it is simply a list of skills that are essential to be a critical thinker. Critical thinking entails solid reasoning and problem solving skills; skepticism; and an ability to identify biases, distortions, omissions, and assumptions. Excellent deductive and inductive reasoning, and problem solving skills contribute to critical thinking. So, you can consider the subject matter of sections 7.2 and 7.3 to be part of critical thinking. Because we will be devoting considerable time to these concepts in the rest of the module, let us begin with a discussion about the other aspects of critical thinking.
Let’s address that first part of the definition. Scientists form hypotheses, or predictions about some possible future observations. Then, they collect data, or information (think of this as making those future observations). They do their best to make unbiased observations using reliable techniques that have been verified by others. Then, and only then, they draw a conclusion about what those observations mean. Oh, and do not forget the most important part. “Conclusion” is probably not the most appropriate word because this conclusion is only tentative. A scientist is always prepared that someone else might come along and produce new observations that would require a new conclusion be drawn. Wow! If you like to be right, you could do a lot worse than using a process like this.
A Critical Thinker’s Toolkit
Now for the second part of the definition. Good critical thinkers (and scientists) rely on a variety of tools to evaluate information. Perhaps the most recognizable tool for critical thinking is skepticism (and this term provides the clearest link to the thinking like a scientist definition, as you are about to see). Some people intend it as an insult when they call someone a skeptic. But if someone calls you a skeptic, if they are using the term correctly, you should consider it a great compliment. Simply put, skepticism is a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided. People from Missouri should recognize this principle, as Missouri is known as the Show-Me State. As a skeptic, you are not inclined to believe something just because someone said so, because someone else believes it, or because it sounds reasonable. You must be persuaded by high quality evidence.
Of course, if that evidence is produced, you have a responsibility as a skeptic to change your belief. Failure to change a belief in the face of good evidence is not skepticism; skepticism has open mindedness at its core. M. Neil Browne and Stuart Keeley (2018) use the term weak sense critical thinking to describe critical thinking behaviors that are used only to strengthen a prior belief. Strong sense critical thinking, on the other hand, has as its goal reaching the best conclusion. Sometimes that means strengthening your prior belief, but sometimes it means changing your belief to accommodate the better evidence.
Many times, a failure to think critically or weak sense critical thinking is related to a bias , an inclination, tendency, leaning, or prejudice. Everybody has biases, but many people are unaware of them. Awareness of your own biases gives you the opportunity to control or counteract them. Unfortunately, however, many people are happy to let their biases creep into their attempts to persuade others; indeed, it is a key part of their persuasive strategy. To see how these biases influence messages, just look at the different descriptions and explanations of the same events given by people of different ages or income brackets, or conservative versus liberal commentators, or by commentators from different parts of the world. Of course, to be successful, these people who are consciously using their biases must disguise them. Even undisguised biases can be difficult to identify, so disguised ones can be nearly impossible.
Here are some common sources of biases:
- Personal values and beliefs. Some people believe that human beings are basically driven to seek power and that they are typically in competition with one another over scarce resources. These beliefs are similar to the world-view that political scientists call “realism.” Other people believe that human beings prefer to cooperate and that, given the chance, they will do so. These beliefs are similar to the world-view known as “idealism.” For many people, these deeply held beliefs can influence, or bias, their interpretations of such wide ranging situations as the behavior of nations and their leaders or the behavior of the driver in the car ahead of you. For example, if your worldview is that people are typically in competition and someone cuts you off on the highway, you may assume that the driver did it purposely to get ahead of you. Other types of beliefs about the way the world is or the way the world should be, for example, political beliefs, can similarly become a significant source of bias.
- Racism, sexism, ageism and other forms of prejudice and bigotry. These are, sadly, a common source of bias in many people. They are essentially a special kind of “belief about the way the world is.” These beliefs—for example, that women do not make effective leaders—lead people to ignore contradictory evidence (examples of effective women leaders, or research that disputes the belief) and to interpret ambiguous evidence in a way consistent with the belief.
- Self-interest. When particular people benefit from things turning out a certain way, they can sometimes be very susceptible to letting that interest bias them. For example, a company that will earn a profit if they sell their product may have a bias in the way that they give information about their product. A union that will benefit if its members get a generous contract might have a bias in the way it presents information about salaries at competing organizations. (Note that our inclusion of examples describing both companies and unions is an explicit attempt to control for our own personal biases). Home buyers are often dismayed to discover that they purchased their dream house from someone whose self-interest led them to lie about flooding problems in the basement or back yard. This principle, the biasing power of self-interest, is likely what led to the famous phrase Caveat Emptor (let the buyer beware) .
Knowing that these types of biases exist will help you evaluate evidence more critically. Do not forget, though, that people are not always keen to let you discover the sources of biases in their arguments. For example, companies or political organizations can sometimes disguise their support of a research study by contracting with a university professor, who comes complete with a seemingly unbiased institutional affiliation, to conduct the study.
People’s biases, conscious or unconscious, can lead them to make omissions, distortions, and assumptions that undermine our ability to correctly evaluate evidence. It is essential that you look for these elements. Always ask, what is missing, what is not as it appears, and what is being assumed here? For example, consider this (fictional) chart from an ad reporting customer satisfaction at 4 local health clubs.
Clearly, from the results of the chart, one would be tempted to give Club C a try, as customer satisfaction is much higher than for the other 3 clubs.
There are so many distortions and omissions in this chart, however, that it is actually quite meaningless. First, how was satisfaction measured? Do the bars represent responses to a survey? If so, how were the questions asked? Most importantly, where is the missing scale for the chart? Although the differences look quite large, are they really?
Well, here is the same chart, with a different scale, this time labeled:
Club C is not so impressive any more, is it? In fact, all of the health clubs have customer satisfaction ratings (whatever that means) between 85% and 88%. In the first chart, the entire scale of the graph included only the percentages between 83 and 89. This “judicious” choice of scale—some would call it a distortion—and omission of that scale from the chart make the tiny differences among the clubs seem important, however.
Also, in order to be a critical thinker, you need to learn to pay attention to the assumptions that underlie a message. Let us briefly illustrate the role of assumptions by touching on some people’s beliefs about the criminal justice system in the US. Some believe that a major problem with our judicial system is that many criminals go free because of legal technicalities. Others believe that a major problem is that many innocent people are convicted of crimes. The simple fact is, both types of errors occur. A person’s conclusion about which flaw in our judicial system is the greater tragedy is based on an assumption about which of these is the more serious error (letting the guilty go free or convicting the innocent). This type of assumption is called a value assumption (Browne and Keeley, 2018). It reflects the differences in values that people develop, differences that may lead us to disregard valid evidence that does not fit in with our particular values.
Oh, by the way, some students probably noticed this, but the seven tips for evaluating information that we shared in Module 1 are related to this. Actually, they are part of this section. The tips are, to a very large degree, set of ideas you can use to help you identify biases, distortions, omissions, and assumptions. If you do not remember this section, we strongly recommend you take a few minutes to review it.
skepticism : a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided
bias : an inclination, tendency, leaning, or prejudice
- Which of your beliefs (or disbeliefs) from the Activate exercise for this section were derived from a process of critical thinking? If some of your beliefs were not based on critical thinking, are you willing to reassess these beliefs? If the answer is no, why do you think that is? If the answer is yes, what concrete steps will you take?
7.2 Reasoning and Judgment
- What percentage of kidnappings are committed by strangers?
- Which area of the house is riskiest: kitchen, bathroom, or stairs?
- What is the most common cancer in the US?
- What percentage of workplace homicides are committed by co-workers?
An essential set of procedural thinking skills is reasoning , the ability to generate and evaluate solid conclusions from a set of statements or evidence. You should note that these conclusions (when they are generated instead of being evaluated) are one key type of inference that we described in Section 7.1. There are two main types of reasoning, deductive and inductive.
Deductive reasoning
Suppose your teacher tells you that if you get an A on the final exam in a course, you will get an A for the whole course. Then, you get an A on the final exam. What will your final course grade be? Most people can see instantly that you can conclude with certainty that you will get an A for the course. This is a type of reasoning called deductive reasoning , which is defined as reasoning in which a conclusion is guaranteed to be true as long as the statements leading to it are true. The three statements can be listed as an argument , with two beginning statements and a conclusion:
Statement 1: If you get an A on the final exam, you will get an A for the course
Statement 2: You get an A on the final exam
Conclusion: You will get an A for the course
This particular arrangement, in which true beginning statements lead to a guaranteed true conclusion, is known as a deductively valid argument . Although deductive reasoning is often the subject of abstract, brain-teasing, puzzle-like word problems, it is actually an extremely important type of everyday reasoning. It is just hard to recognize sometimes. For example, imagine that you are looking for your car keys and you realize that they are either in the kitchen drawer or in your book bag. After looking in the kitchen drawer, you instantly know that they must be in your book bag. That conclusion results from a simple deductive reasoning argument. In addition, solid deductive reasoning skills are necessary for you to succeed in the sciences, philosophy, math, computer programming, and any endeavor involving the use of logic to persuade others to your point of view or to evaluate others’ arguments.
Cognitive psychologists, and before them philosophers, have been quite interested in deductive reasoning, not so much for its practical applications, but for the insights it can offer them about the ways that human beings think. One of the early ideas to emerge from the examination of deductive reasoning is that people learn (or develop) mental versions of rules that allow them to solve these types of reasoning problems (Braine, 1978; Braine, Reiser, & Rumain, 1984). The best way to see this point of view is to realize that there are different possible rules, and some of them are very simple. For example, consider this rule of logic:
therefore q
Logical rules are often presented abstractly, as letters, in order to imply that they can be used in very many specific situations. Here is a concrete version of the of the same rule:
I’ll either have pizza or a hamburger for dinner tonight (p or q)
I won’t have pizza (not p)
Therefore, I’ll have a hamburger (therefore q)
This kind of reasoning seems so natural, so easy, that it is quite plausible that we would use a version of this rule in our daily lives. At least, it seems more plausible than some of the alternative possibilities—for example, that we need to have experience with the specific situation (pizza or hamburger, in this case) in order to solve this type of problem easily. So perhaps there is a form of natural logic (Rips, 1990) that contains very simple versions of logical rules. When we are faced with a reasoning problem that maps onto one of these rules, we use the rule.
But be very careful; things are not always as easy as they seem. Even these simple rules are not so simple. For example, consider the following rule. Many people fail to realize that this rule is just as valid as the pizza or hamburger rule above.
if p, then q
therefore, not p
Concrete version:
If I eat dinner, then I will have dessert
I did not have dessert
Therefore, I did not eat dinner
The simple fact is, it can be very difficult for people to apply rules of deductive logic correctly; as a result, they make many errors when trying to do so. Is this a deductively valid argument or not?
Students who like school study a lot
Students who study a lot get good grades
Jane does not like school
Therefore, Jane does not get good grades
Many people are surprised to discover that this is not a logically valid argument; the conclusion is not guaranteed to be true from the beginning statements. Although the first statement says that students who like school study a lot, it does NOT say that students who do not like school do not study a lot. In other words, it may very well be possible to study a lot without liking school. Even people who sometimes get problems like this right might not be using the rules of deductive reasoning. Instead, they might just be making judgments for examples they know, in this case, remembering instances of people who get good grades despite not liking school.
Making deductive reasoning even more difficult is the fact that there are two important properties that an argument may have. One, it can be valid or invalid (meaning that the conclusion does or does not follow logically from the statements leading up to it). Two, an argument (or more correctly, its conclusion) can be true or false. Here is an example of an argument that is logically valid, but has a false conclusion (at least we think it is false).
Either you are eleven feet tall or the Grand Canyon was created by a spaceship crashing into the earth.
You are not eleven feet tall
Therefore the Grand Canyon was created by a spaceship crashing into the earth
This argument has the exact same form as the pizza or hamburger argument above, making it is deductively valid. The conclusion is so false, however, that it is absurd (of course, the reason the conclusion is false is that the first statement is false). When people are judging arguments, they tend to not observe the difference between deductive validity and the empirical truth of statements or conclusions. If the elements of an argument happen to be true, people are likely to judge the argument logically valid; if the elements are false, they will very likely judge it invalid (Markovits & Bouffard-Bouchard, 1992; Moshman & Franks, 1986). Thus, it seems a stretch to say that people are using these logical rules to judge the validity of arguments. Many psychologists believe that most people actually have very limited deductive reasoning skills (Johnson-Laird, 1999). They argue that when faced with a problem for which deductive logic is required, people resort to some simpler technique, such as matching terms that appear in the statements and the conclusion (Evans, 1982). This might not seem like a problem, but what if reasoners believe that the elements are true and they happen to be wrong; they will would believe that they are using a form of reasoning that guarantees they are correct and yet be wrong.
deductive reasoning : a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true
argument : a set of statements in which the beginning statements lead to a conclusion
deductively valid argument : an argument for which true beginning statements guarantee that the conclusion is true
Inductive reasoning and judgment
Every day, you make many judgments about the likelihood of one thing or another. Whether you realize it or not, you are practicing inductive reasoning on a daily basis. In inductive reasoning arguments, a conclusion is likely whenever the statements preceding it are true. The first thing to notice about inductive reasoning is that, by definition, you can never be sure about your conclusion; you can only estimate how likely the conclusion is. Inductive reasoning may lead you to focus on Memory Encoding and Recoding when you study for the exam, but it is possible the instructor will ask more questions about Memory Retrieval instead. Unlike deductive reasoning, the conclusions you reach through inductive reasoning are only probable, not certain. That is why scientists consider inductive reasoning weaker than deductive reasoning. But imagine how hard it would be for us to function if we could not act unless we were certain about the outcome.
Inductive reasoning can be represented as logical arguments consisting of statements and a conclusion, just as deductive reasoning can be. In an inductive argument, you are given some statements and a conclusion (or you are given some statements and must draw a conclusion). An argument is inductively strong if the conclusion would be very probable whenever the statements are true. So, for example, here is an inductively strong argument:
- Statement #1: The forecaster on Channel 2 said it is going to rain today.
- Statement #2: The forecaster on Channel 5 said it is going to rain today.
- Statement #3: It is very cloudy and humid.
- Statement #4: You just heard thunder.
- Conclusion (or judgment): It is going to rain today.
Think of the statements as evidence, on the basis of which you will draw a conclusion. So, based on the evidence presented in the four statements, it is very likely that it will rain today. Will it definitely rain today? Certainly not. We can all think of times that the weather forecaster was wrong.
A true story: Some years ago psychology student was watching a baseball playoff game between the St. Louis Cardinals and the Los Angeles Dodgers. A graphic on the screen had just informed the audience that the Cardinal at bat, (Hall of Fame shortstop) Ozzie Smith, a switch hitter batting left-handed for this plate appearance, had never, in nearly 3000 career at-bats, hit a home run left-handed. The student, who had just learned about inductive reasoning in his psychology class, turned to his companion (a Cardinals fan) and smugly said, “It is an inductively strong argument that Ozzie Smith will not hit a home run.” He turned back to face the television just in time to watch the ball sail over the right field fence for a home run. Although the student felt foolish at the time, he was not wrong. It was an inductively strong argument; 3000 at-bats is an awful lot of evidence suggesting that the Wizard of Ozz (as he was known) would not be hitting one out of the park (think of each at-bat without a home run as a statement in an inductive argument). Sadly (for the die-hard Cubs fan and Cardinals-hating student), despite the strength of the argument, the conclusion was wrong.
Given the possibility that we might draw an incorrect conclusion even with an inductively strong argument, we really want to be sure that we do, in fact, make inductively strong arguments. If we judge something probable, it had better be probable. If we judge something nearly impossible, it had better not happen. Think of inductive reasoning, then, as making reasonably accurate judgments of the probability of some conclusion given a set of evidence.
We base many decisions in our lives on inductive reasoning. For example:
Statement #1: Psychology is not my best subject
Statement #2: My psychology instructor has a reputation for giving difficult exams
Statement #3: My first psychology exam was much harder than I expected
Judgment: The next exam will probably be very difficult.
Decision: I will study tonight instead of watching Netflix.
Some other examples of judgments that people commonly make in a school context include judgments of the likelihood that:
- A particular class will be interesting/useful/difficult
- You will be able to finish writing a paper by next week if you go out tonight
- Your laptop’s battery will last through the next trip to the library
- You will not miss anything important if you skip class tomorrow
- Your instructor will not notice if you skip class tomorrow
- You will be able to find a book that you will need for a paper
- There will be an essay question about Memory Encoding on the next exam
Tversky and Kahneman (1983) recognized that there are two general ways that we might make these judgments; they termed them extensional (i.e., following the laws of probability) and intuitive (i.e., using shortcuts or heuristics, see below). We will use a similar distinction between Type 1 and Type 2 thinking, as described by Keith Stanovich and his colleagues (Evans and Stanovich, 2013; Stanovich and West, 2000). Type 1 thinking is fast, automatic, effortful, and emotional. In fact, it is hardly fair to call it reasoning at all, as judgments just seem to pop into one’s head. Type 2 thinking , on the other hand, is slow, effortful, and logical. So obviously, it is more likely to lead to a correct judgment, or an optimal decision. The problem is, we tend to over-rely on Type 1. Now, we are not saying that Type 2 is the right way to go for every decision or judgment we make. It seems a bit much, for example, to engage in a step-by-step logical reasoning procedure to decide whether we will have chicken or fish for dinner tonight.
Many bad decisions in some very important contexts, however, can be traced back to poor judgments of the likelihood of certain risks or outcomes that result from the use of Type 1 when a more logical reasoning process would have been more appropriate. For example:
Statement #1: It is late at night.
Statement #2: Albert has been drinking beer for the past five hours at a party.
Statement #3: Albert is not exactly sure where he is or how far away home is.
Judgment: Albert will have no difficulty walking home.
Decision: He walks home alone.
As you can see in this example, the three statements backing up the judgment do not really support it. In other words, this argument is not inductively strong because it is based on judgments that ignore the laws of probability. What are the chances that someone facing these conditions will be able to walk home alone easily? And one need not be drunk to make poor decisions based on judgments that just pop into our heads.
The truth is that many of our probability judgments do not come very close to what the laws of probability say they should be. Think about it. In order for us to reason in accordance with these laws, we would need to know the laws of probability, which would allow us to calculate the relationship between particular pieces of evidence and the probability of some outcome (i.e., how much likelihood should change given a piece of evidence), and we would have to do these heavy math calculations in our heads. After all, that is what Type 2 requires. Needless to say, even if we were motivated, we often do not even know how to apply Type 2 reasoning in many cases.
So what do we do when we don’t have the knowledge, skills, or time required to make the correct mathematical judgment? Do we hold off and wait until we can get better evidence? Do we read up on probability and fire up our calculator app so we can compute the correct probability? Of course not. We rely on Type 1 thinking. We “wing it.” That is, we come up with a likelihood estimate using some means at our disposal. Psychologists use the term heuristic to describe the type of “winging it” we are talking about. A heuristic is a shortcut strategy that we use to make some judgment or solve some problem (see Section 7.3). Heuristics are easy and quick, think of them as the basic procedures that are characteristic of Type 1. They can absolutely lead to reasonably good judgments and decisions in some situations (like choosing between chicken and fish for dinner). They are, however, far from foolproof. There are, in fact, quite a lot of situations in which heuristics can lead us to make incorrect judgments, and in many cases the decisions based on those judgments can have serious consequences.
Let us return to the activity that begins this section. You were asked to judge the likelihood (or frequency) of certain events and risks. You were free to come up with your own evidence (or statements) to make these judgments. This is where a heuristic crops up. As a judgment shortcut, we tend to generate specific examples of those very events to help us decide their likelihood or frequency. For example, if we are asked to judge how common, frequent, or likely a particular type of cancer is, many of our statements would be examples of specific cancer cases:
Statement #1: Andy Kaufman (comedian) had lung cancer.
Statement #2: Colin Powell (US Secretary of State) had prostate cancer.
Statement #3: Bob Marley (musician) had skin and brain cancer
Statement #4: Sandra Day O’Connor (Supreme Court Justice) had breast cancer.
Statement #5: Fred Rogers (children’s entertainer) had stomach cancer.
Statement #6: Robin Roberts (news anchor) had breast cancer.
Statement #7: Bette Davis (actress) had breast cancer.
Judgment: Breast cancer is the most common type.
Your own experience or memory may also tell you that breast cancer is the most common type. But it is not (although it is common). Actually, skin cancer is the most common type in the US. We make the same types of misjudgments all the time because we do not generate the examples or evidence according to their actual frequencies or probabilities. Instead, we have a tendency (or bias) to search for the examples in memory; if they are easy to retrieve, we assume that they are common. To rephrase this in the language of the heuristic, events seem more likely to the extent that they are available to memory. This bias has been termed the availability heuristic (Kahneman and Tversky, 1974).
The fact that we use the availability heuristic does not automatically mean that our judgment is wrong. The reason we use heuristics in the first place is that they work fairly well in many cases (and, of course that they are easy to use). So, the easiest examples to think of sometimes are the most common ones. Is it more likely that a member of the U.S. Senate is a man or a woman? Most people have a much easier time generating examples of male senators. And as it turns out, the U.S. Senate has many more men than women (74 to 26 in 2020). In this case, then, the availability heuristic would lead you to make the correct judgment; it is far more likely that a senator would be a man.
In many other cases, however, the availability heuristic will lead us astray. This is because events can be memorable for many reasons other than their frequency. Section 5.2, Encoding Meaning, suggested that one good way to encode the meaning of some information is to form a mental image of it. Thus, information that has been pictured mentally will be more available to memory. Indeed, an event that is vivid and easily pictured will trick many people into supposing that type of event is more common than it actually is. Repetition of information will also make it more memorable. So, if the same event is described to you in a magazine, on the evening news, on a podcast that you listen to, and in your Facebook feed; it will be very available to memory. Again, the availability heuristic will cause you to misperceive the frequency of these types of events.
Most interestingly, information that is unusual is more memorable. Suppose we give you the following list of words to remember: box, flower, letter, platypus, oven, boat, newspaper, purse, drum, car. Very likely, the easiest word to remember would be platypus, the unusual one. The same thing occurs with memories of events. An event may be available to memory because it is unusual, yet the availability heuristic leads us to judge that the event is common. Did you catch that? In these cases, the availability heuristic makes us think the exact opposite of the true frequency. We end up thinking something is common because it is unusual (and therefore memorable). Yikes.
The misapplication of the availability heuristic sometimes has unfortunate results. For example, if you went to K-12 school in the US over the past 10 years, it is extremely likely that you have participated in lockdown and active shooter drills. Of course, everyone is trying to prevent the tragedy of another school shooting. And believe us, we are not trying to minimize how terrible the tragedy is. But the truth of the matter is, school shootings are extremely rare. Because the federal government does not keep a database of school shootings, the Washington Post has maintained their own running tally. Between 1999 and January 2020 (the date of the most recent school shooting with a death in the US at of the time this paragraph was written), the Post reported a total of 254 people died in school shootings in the US. Not 254 per year, 254 total. That is an average of 12 per year. Of course, that is 254 people who should not have died (particularly because many were children), but in a country with approximately 60,000,000 students and teachers, this is a very small risk.
But many students and teachers are terrified that they will be victims of school shootings because of the availability heuristic. It is so easy to think of examples (they are very available to memory) that people believe the event is very common. It is not. And there is a downside to this. We happen to believe that there is an enormous gun violence problem in the United States. According the the Centers for Disease Control and Prevention, there were 39,773 firearm deaths in the US in 2017. Fifteen of those deaths were in school shootings, according to the Post. 60% of those deaths were suicides. When people pay attention to the school shooting risk (low), they often fail to notice the much larger risk.
And examples like this are by no means unique. The authors of this book have been teaching psychology since the 1990’s. We have been able to make the exact same arguments about the misapplication of the availability heuristics and keep them current by simply swapping out for the “fear of the day.” In the 1990’s it was children being kidnapped by strangers (it was known as “stranger danger”) despite the facts that kidnappings accounted for only 2% of the violent crimes committed against children, and only 24% of kidnappings are committed by strangers (US Department of Justice, 2007). This fear overlapped with the fear of terrorism that gripped the country after the 2001 terrorist attacks on the World Trade Center and US Pentagon and still plagues the population of the US somewhat in 2020. After a well-publicized, sensational act of violence, people are extremely likely to increase their estimates of the chances that they, too, will be victims of terror. Think about the reality, however. In October of 2001, a terrorist mailed anthrax spores to members of the US government and a number of media companies. A total of five people died as a result of this attack. The nation was nearly paralyzed by the fear of dying from the attack; in reality the probability of an individual person dying was 0.00000002.
The availability heuristic can lead you to make incorrect judgments in a school setting as well. For example, suppose you are trying to decide if you should take a class from a particular math professor. You might try to make a judgment of how good a teacher she is by recalling instances of friends and acquaintances making comments about her teaching skill. You may have some examples that suggest that she is a poor teacher very available to memory, so on the basis of the availability heuristic you judge her a poor teacher and decide to take the class from someone else. What if, however, the instances you recalled were all from the same person, and this person happens to be a very colorful storyteller? The subsequent ease of remembering the instances might not indicate that the professor is a poor teacher after all.
Although the availability heuristic is obviously important, it is not the only judgment heuristic we use. Amos Tversky and Daniel Kahneman examined the role of heuristics in inductive reasoning in a long series of studies. Kahneman received a Nobel Prize in Economics for this research in 2002, and Tversky would have certainly received one as well if he had not died of melanoma at age 59 in 1996 (Nobel Prizes are not awarded posthumously). Kahneman and Tversky demonstrated repeatedly that people do not reason in ways that are consistent with the laws of probability. They identified several heuristic strategies that people use instead to make judgments about likelihood. The importance of this work for economics (and the reason that Kahneman was awarded the Nobel Prize) is that earlier economic theories had assumed that people do make judgments rationally, that is, in agreement with the laws of probability.
Another common heuristic that people use for making judgments is the representativeness heuristic (Kahneman & Tversky 1973). Suppose we describe a person to you. He is quiet and shy, has an unassuming personality, and likes to work with numbers. Is this person more likely to be an accountant or an attorney? If you said accountant, you were probably using the representativeness heuristic. Our imaginary person is judged likely to be an accountant because he resembles, or is representative of the concept of, an accountant. When research participants are asked to make judgments such as these, the only thing that seems to matter is the representativeness of the description. For example, if told that the person described is in a room that contains 70 attorneys and 30 accountants, participants will still assume that he is an accountant.
inductive reasoning : a type of reasoning in which we make judgments about likelihood from sets of evidence
inductively strong argument : an inductive argument in which the beginning statements lead to a conclusion that is probably true
heuristic : a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions
availability heuristic : judging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)
representativeness heuristic: judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)
Type 1 thinking : fast, automatic, and emotional thinking.
Type 2 thinking : slow, effortful, and logical thinking.
- What percentage of workplace homicides are co-worker violence?
Many people get these questions wrong. The answers are 10%; stairs; skin; 6%. How close were your answers? Explain how the availability heuristic might have led you to make the incorrect judgments.
- Can you think of some other judgments that you have made (or beliefs that you have) that might have been influenced by the availability heuristic?
7.3 Problem Solving
- Please take a few minutes to list a number of problems that you are facing right now.
- Now write about a problem that you recently solved.
- What is your definition of a problem?
Mary has a problem. Her daughter, ordinarily quite eager to please, appears to delight in being the last person to do anything. Whether getting ready for school, going to piano lessons or karate class, or even going out with her friends, she seems unwilling or unable to get ready on time. Other people have different kinds of problems. For example, many students work at jobs, have numerous family commitments, and are facing a course schedule full of difficult exams, assignments, papers, and speeches. How can they find enough time to devote to their studies and still fulfill their other obligations? Speaking of students and their problems: Show that a ball thrown vertically upward with initial velocity v0 takes twice as much time to return as to reach the highest point (from Spiegel, 1981).
These are three very different situations, but we have called them all problems. What makes them all the same, despite the differences? A psychologist might define a problem as a situation with an initial state, a goal state, and a set of possible intermediate states. Somewhat more meaningfully, we might consider a problem a situation in which you are in here one state (e.g., daughter is always late), you want to be there in another state (e.g., daughter is not always late), and with no obvious way to get from here to there. Defined this way, each of the three situations we outlined can now be seen as an example of the same general concept, a problem. At this point, you might begin to wonder what is not a problem, given such a general definition. It seems that nearly every non-routine task we engage in could qualify as a problem. As long as you realize that problems are not necessarily bad (it can be quite fun and satisfying to rise to the challenge and solve a problem), this may be a useful way to think about it.
Can we identify a set of problem-solving skills that would apply to these very different kinds of situations? That task, in a nutshell, is a major goal of this section. Let us try to begin to make sense of the wide variety of ways that problems can be solved with an important observation: the process of solving problems can be divided into two key parts. First, people have to notice, comprehend, and represent the problem properly in their minds (called problem representation ). Second, they have to apply some kind of solution strategy to the problem. Psychologists have studied both of these key parts of the process in detail.
When you first think about the problem-solving process, you might guess that most of our difficulties would occur because we are failing in the second step, the application of strategies. Although this can be a significant difficulty much of the time, the more important source of difficulty is probably problem representation. In short, we often fail to solve a problem because we are looking at it, or thinking about it, the wrong way.
problem : a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)
problem representation : noticing, comprehending and forming a mental conception of a problem
Defining and Mentally Representing Problems in Order to Solve Them
So, the main obstacle to solving a problem is that we do not clearly understand exactly what the problem is. Recall the problem with Mary’s daughter always being late. One way to represent, or to think about, this problem is that she is being defiant. She refuses to get ready in time. This type of representation or definition suggests a particular type of solution. Another way to think about the problem, however, is to consider the possibility that she is simply being sidetracked by interesting diversions. This different conception of what the problem is (i.e., different representation) suggests a very different solution strategy. For example, if Mary defines the problem as defiance, she may be tempted to solve the problem using some kind of coercive tactics, that is, to assert her authority as her mother and force her to listen. On the other hand, if Mary defines the problem as distraction, she may try to solve it by simply removing the distracting objects.
As you might guess, when a problem is represented one way, the solution may seem very difficult, or even impossible. Seen another way, the solution might be very easy. For example, consider the following problem (from Nasar, 1998):
Two bicyclists start 20 miles apart and head toward each other, each going at a steady rate of 10 miles per hour. At the same time, a fly that travels at a steady 15 miles per hour starts from the front wheel of the southbound bicycle and flies to the front wheel of the northbound one, then turns around and flies to the front wheel of the southbound one again, and continues in this manner until he is crushed between the two front wheels. Question: what total distance did the fly cover?
Please take a few minutes to try to solve this problem.
Most people represent this problem as a question about a fly because, well, that is how the question is asked. The solution, using this representation, is to figure out how far the fly travels on the first leg of its journey, then add this total to how far it travels on the second leg of its journey (when it turns around and returns to the first bicycle), then continue to add the smaller distance from each leg of the journey until you converge on the correct answer. You would have to be quite skilled at math to solve this problem, and you would probably need some time and pencil and paper to do it.
If you consider a different representation, however, you can solve this problem in your head. Instead of thinking about it as a question about a fly, think about it as a question about the bicycles. They are 20 miles apart, and each is traveling 10 miles per hour. How long will it take for the bicycles to reach each other? Right, one hour. The fly is traveling 15 miles per hour; therefore, it will travel a total of 15 miles back and forth in the hour before the bicycles meet. Represented one way (as a problem about a fly), the problem is quite difficult. Represented another way (as a problem about two bicycles), it is easy. Changing your representation of a problem is sometimes the best—sometimes the only—way to solve it.
Unfortunately, however, changing a problem’s representation is not the easiest thing in the world to do. Often, problem solvers get stuck looking at a problem one way. This is called fixation . Most people who represent the preceding problem as a problem about a fly probably do not pause to reconsider, and consequently change, their representation. A parent who thinks her daughter is being defiant is unlikely to consider the possibility that her behavior is far less purposeful.
Problem-solving fixation was examined by a group of German psychologists called Gestalt psychologists during the 1930’s and 1940’s. Karl Dunker, for example, discovered an important type of failure to take a different perspective called functional fixedness . Imagine being a participant in one of his experiments. You are asked to figure out how to mount two candles on a door and are given an assortment of odds and ends, including a small empty cardboard box and some thumbtacks. Perhaps you have already figured out a solution: tack the box to the door so it forms a platform, then put the candles on top of the box. Most people are able to arrive at this solution. Imagine a slight variation of the procedure, however. What if, instead of being empty, the box had matches in it? Most people given this version of the problem do not arrive at the solution given above. Why? Because it seems to people that when the box contains matches, it already has a function; it is a matchbox. People are unlikely to consider a new function for an object that already has a function. This is functional fixedness.
Mental set is a type of fixation in which the problem solver gets stuck using the same solution strategy that has been successful in the past, even though the solution may no longer be useful. It is commonly seen when students do math problems for homework. Often, several problems in a row require the reapplication of the same solution strategy. Then, without warning, the next problem in the set requires a new strategy. Many students attempt to apply the formerly successful strategy on the new problem and therefore cannot come up with a correct answer.
The thing to remember is that you cannot solve a problem unless you correctly identify what it is to begin with (initial state) and what you want the end result to be (goal state). That may mean looking at the problem from a different angle and representing it in a new way. The correct representation does not guarantee a successful solution, but it certainly puts you on the right track.
A bit more optimistically, the Gestalt psychologists discovered what may be considered the opposite of fixation, namely insight . Sometimes the solution to a problem just seems to pop into your head. Wolfgang Kohler examined insight by posing many different problems to chimpanzees, principally problems pertaining to their acquisition of out-of-reach food. In one version, a banana was placed outside of a chimpanzee’s cage and a short stick inside the cage. The stick was too short to retrieve the banana, but was long enough to retrieve a longer stick also located outside of the cage. This second stick was long enough to retrieve the banana. After trying, and failing, to reach the banana with the shorter stick, the chimpanzee would try a couple of random-seeming attempts, react with some apparent frustration or anger, then suddenly rush to the longer stick, the correct solution fully realized at this point. This sudden appearance of the solution, observed many times with many different problems, was termed insight by Kohler.
Lest you think it pertains to chimpanzees only, Karl Dunker demonstrated that children also solve problems through insight in the 1930s. More importantly, you have probably experienced insight yourself. Think back to a time when you were trying to solve a difficult problem. After struggling for a while, you gave up. Hours later, the solution just popped into your head, perhaps when you were taking a walk, eating dinner, or lying in bed.
fixation : when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)
functional fixedness : a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function
mental set : a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past
insight : a sudden realization of a solution to a problem
Solving Problems by Trial and Error
Correctly identifying the problem and your goal for a solution is a good start, but recall the psychologist’s definition of a problem: it includes a set of possible intermediate states. Viewed this way, a problem can be solved satisfactorily only if one can find a path through some of these intermediate states to the goal. Imagine a fairly routine problem, finding a new route to school when your ordinary route is blocked (by road construction, for example). At each intersection, you may turn left, turn right, or go straight. A satisfactory solution to the problem (of getting to school) is a sequence of selections at each intersection that allows you to wind up at school.
If you had all the time in the world to get to school, you might try choosing intermediate states randomly. At one corner you turn left, the next you go straight, then you go left again, then right, then right, then straight. Unfortunately, trial and error will not necessarily get you where you want to go, and even if it does, it is not the fastest way to get there. For example, when a friend of ours was in college, he got lost on the way to a concert and attempted to find the venue by choosing streets to turn onto randomly (this was long before the use of GPS). Amazingly enough, the strategy worked, although he did end up missing two out of the three bands who played that night.
Trial and error is not all bad, however. B.F. Skinner, a prominent behaviorist psychologist, suggested that people often behave randomly in order to see what effect the behavior has on the environment and what subsequent effect this environmental change has on them. This seems particularly true for the very young person. Picture a child filling a household’s fish tank with toilet paper, for example. To a child trying to develop a repertoire of creative problem-solving strategies, an odd and random behavior might be just the ticket. Eventually, the exasperated parent hopes, the child will discover that many of these random behaviors do not successfully solve problems; in fact, in many cases they create problems. Thus, one would expect a decrease in this random behavior as a child matures. You should realize, however, that the opposite extreme is equally counterproductive. If the children become too rigid, never trying something unexpected and new, their problem solving skills can become too limited.
Effective problem solving seems to call for a happy medium that strikes a balance between using well-founded old strategies and trying new ground and territory. The individual who recognizes a situation in which an old problem-solving strategy would work best, and who can also recognize a situation in which a new untested strategy is necessary is halfway to success.
Solving Problems with Algorithms and Heuristics
For many problems there is a possible strategy available that will guarantee a correct solution. For example, think about math problems. Math lessons often consist of step-by-step procedures that can be used to solve the problems. If you apply the strategy without error, you are guaranteed to arrive at the correct solution to the problem. This approach is called using an algorithm , a term that denotes the step-by-step procedure that guarantees a correct solution. Because algorithms are sometimes available and come with a guarantee, you might think that most people use them frequently. Unfortunately, however, they do not. As the experience of many students who have struggled through math classes can attest, algorithms can be extremely difficult to use, even when the problem solver knows which algorithm is supposed to work in solving the problem. In problems outside of math class, we often do not even know if an algorithm is available. It is probably fair to say, then, that algorithms are rarely used when people try to solve problems.
Because algorithms are so difficult to use, people often pass up the opportunity to guarantee a correct solution in favor of a strategy that is much easier to use and yields a reasonable chance of coming up with a correct solution. These strategies are called problem solving heuristics . Similar to what you saw in section 6.2 with reasoning heuristics, a problem solving heuristic is a shortcut strategy that people use when trying to solve problems. It usually works pretty well, but does not guarantee a correct solution to the problem. For example, one problem solving heuristic might be “always move toward the goal” (so when trying to get to school when your regular route is blocked, you would always turn in the direction you think the school is). A heuristic that people might use when doing math homework is “use the same solution strategy that you just used for the previous problem.”
By the way, we hope these last two paragraphs feel familiar to you. They seem to parallel a distinction that you recently learned. Indeed, algorithms and problem-solving heuristics are another example of the distinction between Type 1 thinking and Type 2 thinking.
Although it is probably not worth describing a large number of specific heuristics, two observations about heuristics are worth mentioning. First, heuristics can be very general or they can be very specific, pertaining to a particular type of problem only. For example, “always move toward the goal” is a general strategy that you can apply to countless problem situations. On the other hand, “when you are lost without a functioning gps, pick the most expensive car you can see and follow it” is specific to the problem of being lost. Second, all heuristics are not equally useful. One heuristic that many students know is “when in doubt, choose c for a question on a multiple-choice exam.” This is a dreadful strategy because many instructors intentionally randomize the order of answer choices. Another test-taking heuristic, somewhat more useful, is “look for the answer to one question somewhere else on the exam.”
You really should pay attention to the application of heuristics to test taking. Imagine that while reviewing your answers for a multiple-choice exam before turning it in, you come across a question for which you originally thought the answer was c. Upon reflection, you now think that the answer might be b. Should you change the answer to b, or should you stick with your first impression? Most people will apply the heuristic strategy to “stick with your first impression.” What they do not realize, of course, is that this is a very poor strategy (Lilienfeld et al, 2009). Most of the errors on exams come on questions that were answered wrong originally and were not changed (so they remain wrong). There are many fewer errors where we change a correct answer to an incorrect answer. And, of course, sometimes we change an incorrect answer to a correct answer. In fact, research has shown that it is more common to change a wrong answer to a right answer than vice versa (Bruno, 2001).
The belief in this poor test-taking strategy (stick with your first impression) is based on the confirmation bias (Nickerson, 1998; Wason, 1960). You first saw the confirmation bias in Module 1, but because it is so important, we will repeat the information here. People have a bias, or tendency, to notice information that confirms what they already believe. Somebody at one time told you to stick with your first impression, so when you look at the results of an exam you have taken, you will tend to notice the cases that are consistent with that belief. That is, you will notice the cases in which you originally had an answer correct and changed it to the wrong answer. You tend not to notice the other two important (and more common) cases, changing an answer from wrong to right, and leaving a wrong answer unchanged.
Because heuristics by definition do not guarantee a correct solution to a problem, mistakes are bound to occur when we employ them. A poor choice of a specific heuristic will lead to an even higher likelihood of making an error.
algorithm : a step-by-step procedure that guarantees a correct solution to a problem
problem solving heuristic : a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions
confirmation bias : people’s tendency to notice information that confirms what they already believe
An Effective Problem-Solving Sequence
You may be left with a big question: If algorithms are hard to use and heuristics often don’t work, how am I supposed to solve problems? Robert Sternberg (1996), as part of his theory of what makes people successfully intelligent (Module 8) described a problem-solving sequence that has been shown to work rather well:
- Identify the existence of a problem. In school, problem identification is often easy; problems that you encounter in math classes, for example, are conveniently labeled as problems for you. Outside of school, however, realizing that you have a problem is a key difficulty that you must get past in order to begin solving it. You must be very sensitive to the symptoms that indicate a problem.
- Define the problem. Suppose you realize that you have been having many headaches recently. Very likely, you would identify this as a problem. If you define the problem as “headaches,” the solution would probably be to take aspirin or ibuprofen or some other anti-inflammatory medication. If the headaches keep returning, however, you have not really solved the problem—likely because you have mistaken a symptom for the problem itself. Instead, you must find the root cause of the headaches. Stress might be the real problem. For you to successfully solve many problems it may be necessary for you to overcome your fixations and represent the problems differently. One specific strategy that you might find useful is to try to define the problem from someone else’s perspective. How would your parents, spouse, significant other, doctor, etc. define the problem? Somewhere in these different perspectives may lurk the key definition that will allow you to find an easier and permanent solution.
- Formulate strategy. Now it is time to begin planning exactly how the problem will be solved. Is there an algorithm or heuristic available for you to use? Remember, heuristics by their very nature guarantee that occasionally you will not be able to solve the problem. One point to keep in mind is that you should look for long-range solutions, which are more likely to address the root cause of a problem than short-range solutions.
- Represent and organize information. Similar to the way that the problem itself can be defined, or represented in multiple ways, information within the problem is open to different interpretations. Suppose you are studying for a big exam. You have chapters from a textbook and from a supplemental reader, along with lecture notes that all need to be studied. How should you (represent and) organize these materials? Should you separate them by type of material (text versus reader versus lecture notes), or should you separate them by topic? To solve problems effectively, you must learn to find the most useful representation and organization of information.
- Allocate resources. This is perhaps the simplest principle of the problem solving sequence, but it is extremely difficult for many people. First, you must decide whether time, money, skills, effort, goodwill, or some other resource would help to solve the problem Then, you must make the hard choice of deciding which resources to use, realizing that you cannot devote maximum resources to every problem. Very often, the solution to problem is simply to change how resources are allocated (for example, spending more time studying in order to improve grades).
- Monitor and evaluate solutions. Pay attention to the solution strategy while you are applying it. If it is not working, you may be able to select another strategy. Another fact you should realize about problem solving is that it never does end. Solving one problem frequently brings up new ones. Good monitoring and evaluation of your problem solutions can help you to anticipate and get a jump on solving the inevitable new problems that will arise.
Please note that this as an effective problem-solving sequence, not the effective problem solving sequence. Just as you can become fixated and end up representing the problem incorrectly or trying an inefficient solution, you can become stuck applying the problem-solving sequence in an inflexible way. Clearly there are problem situations that can be solved without using these skills in this order.
Additionally, many real-world problems may require that you go back and redefine a problem several times as the situation changes (Sternberg et al. 2000). For example, consider the problem with Mary’s daughter one last time. At first, Mary did represent the problem as one of defiance. When her early strategy of pleading and threatening punishment was unsuccessful, Mary began to observe her daughter more carefully. She noticed that, indeed, her daughter’s attention would be drawn by an irresistible distraction or book. Fresh with a re-representation of the problem, she began a new solution strategy. She began to remind her daughter every few minutes to stay on task and remind her that if she is ready before it is time to leave, she may return to the book or other distracting object at that time. Fortunately, this strategy was successful, so Mary did not have to go back and redefine the problem again.
Pick one or two of the problems that you listed when you first started studying this section and try to work out the steps of Sternberg’s problem solving sequence for each one.
a mental representation of a category of things in the world
an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning
knowledge about one’s own cognitive processes; thinking about your thinking
individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do
Thinking like a scientist in your everyday life for the purpose of drawing correct conclusions. It entails skepticism; an ability to identify biases, distortions, omissions, and assumptions; and excellent deductive and inductive reasoning, and problem solving skills.
a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided
an inclination, tendency, leaning, or prejudice
a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true
a set of statements in which the beginning statements lead to a conclusion
an argument for which true beginning statements guarantee that the conclusion is true
a type of reasoning in which we make judgments about likelihood from sets of evidence
an inductive argument in which the beginning statements lead to a conclusion that is probably true
fast, automatic, and emotional thinking
slow, effortful, and logical thinking
a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions
udging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)
judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)
a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)
noticing, comprehending and forming a mental conception of a problem
when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)
a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function
a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past
a sudden realization of a solution to a problem
a step-by-step procedure that guarantees a correct solution to a problem
The tendency to notice and pay attention to information that confirms your prior beliefs and to ignore information that disconfirms them.
a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions
Introduction to Psychology Copyright © 2020 by Ken Gray; Elizabeth Arnott-Hill; and Or'Shaundra Benson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
Share This Book
Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.
7 Thinking, Language, and Problem Solving
What is the best way to solve a problem? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these and are called cognitive psychologists.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Table of Contents
7.1 What is Cognition? 7.2 Language 7.3 Problem Solving
7.1 What is Cognition?
Learning Objectives
By the end of this section, you will be able to:
- Describe cognition
- Distinguish concepts and prototypes
- Explain the difference between natural and artificial concepts
- Describe how schemata are organized and constructed
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition . Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).
Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.
Concepts and Prototypes
The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the mind synthesizes information from emotions and memories ( Figure 7.2 ). Emotion and memory are powerful influences on both our thoughts and behaviors.
Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.
Concepts can be complex and abstract, like justice, or more concrete, like types of birds. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.
Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype is the best example or representation of a concept. For example, what comes to your mind when you think of a dog? Most likely your early experiences with dogs will shape what you imagine. If your first pet was a Golden Retriever, there is a good chance that this would be your prototype for the category of dogs.
Natural and Artificial Concepts
In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations, experiences with snow, or indirect knowledge (such as from films or books) ( Figure 7.3 ).
An artificial concept , on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.
A schema (plural: schemata) is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.
An event schema , also known as a cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure 7.4 ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)
Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure 7.5 ).
Remember the elevator? It feels almost impossible to walk in and not face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.
7.2 LAnguage
- Define language and demonstrate familiarity with the components of language
- Understand the development of language
- Explain the relationship between language and thinking
Language is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.
Components of Language
Language, be it spoken, signed, or written, has specific components: a lexicon and lexicon grammar . Lexicon refers to the words of a given language. Thus, lexicon is a language’s vocabulary. Grammar refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.
Words are formed by combining the various phonemes that make up the language. A phoneme (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. For example, the phoneme English speakers associate with the letter ‘L’ is not used in the Japanese language. Similarly, many Southern African languages use phonemes, sometimes referred to as ‘click consonants’ that are not used in English.
Phonemes are combined to form morphemes , which are the smallest units of language that convey some type of meaning. Some words are morphemes, but not all morphemes are words. For example, “-ed” is a morpheme used to convey the past-tense in English, but it is not a word. The word “review” contains two morphemes: re- (meaning to do something again) and view (to see). Finally, some words like “I” and “a” are both a phonemes and morphemes.
We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar. Semantics refers to the process by which we derive meaning from morphemes and words by connecting those morphemes and words to stored concepts. Syntax refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011). For example, you would never say “the dog walked I today” to let someone know you took your dog for a walk–that sentence does not obey English syntax and is therefore difficult to make sense of.
We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. Through our use of words and language, we are able to form, organize, and express ideas, schema, and artificial concepts.
Language Development
Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F. Skinner (1957) proposed that language is learned through reinforcement. Noam Chomsky (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).
Children begin to learn about language from a very early age ( Table 7.1 ). In fact, it appears that this is occurring even before we are born. Newborns show preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).
Stages of Language and Communication Development | ||
---|---|---|
Stage | Age | Developmental Language and Communication |
1 | 0–3 months | Reflexive communication |
2 | 3–8 months | Reflexive communication; interest in others |
3 | 8–13 months | Intentional communication; sociability |
4 | 12–18 months | First words |
5 | 18–24 months | Simple sentences of two words |
6 | 2–3 years | Sentences of three or more words |
7 | 3–5 years | Complex sentences; has conversations |
DIG DEEPER: The Case of Genie
In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.
Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.
You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).
After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).
Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.
As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).
Language and Thought
When we speak one language, we agree that words are representations of ideas, people, places, and events. The given language that children learn is connected to their culture and surroundings. But can words themselves shape the way we think about things? Psychologists have long investigated the question of whether language shapes thoughts and actions, or whether our thoughts and beliefs shape our language. Two researchers, Edward Sapir and Benjamin Lee Whorf, began this investigation in the 1940s. They wanted to understand how the language habits of a community encourage members of that community to interpret language in a particular manner (Sapir, 1941/1964). Sapir and Whorf proposed that language determines thought. For example, in some languages there are many different words for love. However, in English we use the word love for all types of love. Does this affect how we think about love depending on the language that we speak (Whorf, 1956)? Researchers have since identified this view as too absolute, pointing out a lack of empiricism behind what Sapir and Whorf proposed (Abler, 2013; Boroditsky, 2011; van Troyer, 1994). Today, psychologists continue to study and debate the relationship between language and thought.
WHAT DO YOU THINK? The Meaning of Language
Think about what you know of other languages; perhaps you even speak multiple languages. Imagine for a moment that your closest friend fluently speaks more than one language. Do you think that friend thinks differently, depending on which language is being spoken? You may know a few words that are not translatable from their original language into English. For example, the Portuguese word saudade originated during the 15th century, when Portuguese sailors left home to explore the seas and travel to Africa or Asia. Those left behind described the emptiness and fondness they felt as saudade ( Figure 7.6 ) . The word came to express many meanings, including loss, nostalgia, yearning, warm memories, and hope. There is no single word in English that includes all of those emotions in a single description. Do words such as saudade indicate that different languages produce different patterns of thought in people? What do you think??
One group of researchers who wanted to investigate how language influences thought compared how English speakers and the Dani people of Papua New Guinea think and speak about color. The Dani have two words for color: one word for light and one word for dark . In contrast, the English language has 11 color words. Researchers hypothesized that the number of color terms could limit the ways that the Dani people conceptualized color. However, the Dani were able to distinguish colors with the same ability as English speakers, despite having fewer words at their disposal (Berlin & Kay, 1969). A recent review of research aimed at determining how language might affect something like color perception suggests that language can influence perceptual phenomena, especially in the left hemisphere of the brain. You may recall from earlier chapters that the left hemisphere is associated with language for most people. However, the right (less linguistic hemisphere) of the brain is less affected by linguistic influences on perception (Regier & Kay, 2009)
7.3 Problem Solving
- Describe problem solving strategies
- Define algorithm and heuristic
- Explain some common roadblocks to effective problem solving and decision making
People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.
Problem-Solving Strategies
When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.
A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.
Problem-Solving Strategies | ||
---|---|---|
Method | Description | Example |
Trial and error | Continue trying different solutions until problem is solved | Restarting phone, turning off WiFi, turning off bluetooth in order to determine why your phone is malfunctioning |
Algorithm | Step-by-step problem-solving formula | Instruction manual for installing new software on your computer |
Heuristic | General problem-solving framework | Working backwards; breaking a task into steps |
Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?
A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
- When one is faced with too much information
- When the time to make a decision is limited
- When the decision to be made is unimportant
- When there is access to very little information to use in making the decision
- When an appropriate heuristic happens to come to mind in the same moment
Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.
Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.
EVERYDAY CONNECTION: Solving Puzzles
Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.7 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.
Here is another popular type of puzzle ( Figure 7.8 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:
Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.
Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. Duncker (1945) conducted foundational research on functional fixedness. He created an experiment in which participants were given a candle, a book of matches, and a box of thumbtacks. They were instructed to use those items to attach the candle to the wall so that it did not drip wax onto the table below. Participants had to use functional fixedness to solve the problem ( Figure 7.10 ). During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.
Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.
The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in Table 7.3 .
Summary of Decision Biases | |
---|---|
Bias | Description |
Anchoring | Tendency to focus on one particular piece of information when making decisions or problem-solving |
Confirmation | Focuses on information that confirms existing beliefs |
Hindsight | Belief that the event just experienced was predictable |
Representative | Unintentional stereotyping of someone or something |
Availability | Decision is based upon either an available precedent or an example that may be faulty |
Were you able to determine how many marbles are needed to balance the scales in Figure 7.9 ? You need nine. Were you able to solve the problems in Figure 7.7 and Figure 7.8 ? Here are the answers ( Figure 7.11 ).
Chapter Summary
7.1 what is cognition.
In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain’s ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to “think twice” about them. Schemata show up in social situations and routines of daily behavior.
7.2 Language
Language is a communication system that has both a lexicon and a system of grammar. Language acquisition occurs naturally and effortlessly during the early stages of life, and this acquisition occurs in a predictable sequence for individuals around the world. Language has a strong influence on thought, and the concept of how language may influence cognition remains an area of study and debate in psychology.
Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.
thinking; or, all of the processes associated with perception, knowledge, problem solving, judgement, language, and memory.
A modern school of psychological thought that empirically examines mental processes such as perception, memory, language, and judgement.
a category or grouping of linguistic information, images, ideas or memories, such as life experiences.
knowledge about words, concepts, and language-based knowledge and facts
the best example or representation of a concept, specific to an individual
concepts developed through direct or indirect experiences with the world
a concept defined by a specific set of characteristics.
a mental construct consisting of a cluster of related concepts
a set of ideas relating to how individuals in certain roles will behave.
also known as a cognitive script; a set of behaviors associated with a particular place or event
also known as an event schema; a set of behaviors associated with a particular place or event
a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another.
the words of a language
the rules of a language used to convey meaning through the use of the lexicon
the basic sounds that make up a language
the smallest unit of language that conveys meaning
the process by which we derive meaning from morphemes and words
the rules guiding the organization of morphemes into words and words into sentences.
Psychology 2e Copyright © 2020 by Openstax is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
Share This Book
7.3 Problem-Solving
Learning objectives.
By the end of this section, you will be able to:
- Describe problem solving strategies
- Define algorithm and heuristic
- Explain some common roadblocks to effective problem solving
People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.
The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.
PROBLEM-SOLVING STRATEGIES
When people are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.
Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.
A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.
Method | Description | Example |
---|---|---|
Trial and error | Continue trying different solutions until problem is solved | Restarting phone, turning off WiFi, turning off bluetooth in order to determine why your phone is malfunctioning |
Algorithm | Step-by-step problem-solving formula | Instruction manual for installing new software on your computer |
Heuristic | General problem-solving framework | Working backwards; breaking a task into steps |
Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?
A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
- When one is faced with too much information
- When the time to make a decision is limited
- When the decision to be made is unimportant
- When there is access to very little information to use in making the decision
- When an appropriate heuristic happens to come to mind in the same moment
Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.
Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.
Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.
Additional Problem Solving Strategies :
- Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
- Analogy – is using a solution that solves a similar problem.
- Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
- Divide and conquer – breaking down large complex problems into smaller more manageable problems.
- Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
- Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
- Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
- Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
- Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
- Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
- Reduction – adapting the problem to be as similar problems where a solution exists.
- Research – using existing knowledge or solutions to similar problems to solve the problem.
- Root cause analysis – trying to identify the cause of the problem.
The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.
One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :
Missionary-Cannibal Problem
Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.
Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.
The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:
- 1. Only one disk can be moved at a time.
- 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
- 3. No disc may be placed on top of a smaller disk.
Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.
Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.
The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.
GESTALT PSYCHOLOGY AND PROBLEM SOLVING
As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.
As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage (1990) suggesting that while collecting data for what would later be his book The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.
While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).
While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.
Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground. Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).
Solving puzzles.
Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (see figure) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.
How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)
Here is another popular type of puzzle (figure below) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:
Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.
Take a look at the “Puzzling Scales” logic puzzle below (figure below). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).
What steps did you take to solve this puzzle? You can read the solution at the end of this section.
Pitfalls to problem solving.
Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.
Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.
Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.
The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.
Bias | Description |
---|---|
Anchoring | Tendency to focus on one particular piece of information when making decisions or problem-solving |
Confirmation | Focuses on information that confirms existing beliefs |
Hindsight | Belief that the event just experienced was predictable |
Representative | Unintentional stereotyping of someone or something |
Availability | Decision is based upon either an available precedent or an example that may be faulty |
Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.
Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.
References:
Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology
Review Questions:
1. A specific formula for solving a problem is called ________.
a. an algorithm
b. a heuristic
c. a mental set
d. trial and error
2. Solving the Tower of Hanoi problem tends to utilize a ________ strategy of problem solving.
a. divide and conquer
b. means-end analysis
d. experiment
3. A mental shortcut in the form of a general problem-solving framework is called ________.
4. Which type of bias involves becoming fixated on a single trait of a problem?
a. anchoring bias
b. confirmation bias
c. representative bias
d. availability bias
5. Which type of bias involves relying on a false stereotype to make a decision?
6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.
a. social adjustment
b. student load payment options
c. emotional learning
d. insight learning
7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.
a. functional fixedness
c. working memory
Critical Thinking Questions:
1. What is functional fixedness and how can overcoming it help you solve problems?
2. How does an algorithm save you time and energy when solving a problem?
Personal Application Question:
1. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?
anchoring bias
availability heuristic
confirmation bias
functional fixedness
hindsight bias
problem-solving strategy
representative bias
trial and error
working backwards
Answers to Exercises
algorithm: problem-solving strategy characterized by a specific set of instructions
anchoring bias: faulty heuristic in which you fixate on a single aspect of a problem to find a solution
availability heuristic: faulty heuristic in which you make a decision based on information readily available to you
confirmation bias: faulty heuristic in which you focus on information that confirms your beliefs
functional fixedness: inability to see an object as useful for any other use other than the one for which it was intended
heuristic: mental shortcut that saves time when solving a problem
hindsight bias: belief that the event just experienced was predictable, even though it really wasn’t
mental set: continually using an old solution to a problem without results
problem-solving strategy: method for solving problems
representative bias: faulty heuristic in which you stereotype someone or something without a valid basis for your judgment
trial and error: problem-solving strategy in which multiple solutions are attempted until the correct one is found
working backwards: heuristic in which you begin to solve a problem by focusing on the end result
Share This Book
- Increase Font Size
Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.
Ch 8: Thinking and Language
Thinking and language.
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such as when you’re driving? Why is it hard to pay attention to a conversation when typing out a text message? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these.
As a part of this discussion, we will consider thinking, and briefly explore the development and use of language. We will also discuss problem solving and creativity. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Learning Objectives
- Understand why selective attention is important and how it can be studied.
- Learn about different models of when and how selection can occur.
- Understand how divided attention or multitasking is studied, and implications of multitasking in situations such as distracted driving.
Thinking and Problem-Solving
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).
- Distinguish between concepts and prototypes
- Explain the difference between natural and artificial concepts
- Describe problem solving strategies, including algorithms and heuristics
- Explain some common roadblocks to effective problem solving
What is Cognition?
Categories and concepts, concepts and prototypes.
The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nerve impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the brain also pulls information from emotions and memories (Figure 9). Emotion and memory are powerful influences on both our thoughts and behaviors.
In order to organize this staggering amount of information, the brain has developed a file cabinet of sorts in the mind. The different files stored in the file cabinet are called concepts. Concepts are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.
Concepts are informed by our semantic memory (you will learn more about this concept when you study memory) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.
Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget’s stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.
Concepts are at the core of intelligent behavior. We expect people to be able to know what to do in new situations and when confronting new objects. If you go into a new classroom and see chairs, a blackboard, a projector, and a screen, you know what these things are and how they will be used. You’ll sit on one of the chairs and expect the instructor to write on the blackboard or project something onto the screen. You do this even if you have never seen any of these particular objects before , because you have concepts of classrooms, chairs, projectors, and so forth, that tell you what they are and what you’re supposed to do with them. Furthermore, if someone tells you a new fact about the projector—for example, that it has a halogen bulb—you are likely to extend this fact to other projectors you encounter. In short, concepts allow you to extend what you have learned about a limited number of objects to a potentially infinite set of entities.
Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype is the best example or representation of a concept. For example, for the category of civil disobedience, your prototype could be Rosa Parks. Her peaceful resistance to segregation on a city bus in Montgomery, Alabama, is a recognizable example of civil disobedience. Or your prototype could be Mohandas Gandhi, sometimes called Mahatma Gandhi (“Mahatma” is an honorific title) (Figure 10).
Mohandas Gandhi served as a nonviolent force for independence for India while simultaneously demanding that Buddhist, Hindu, Muslim, and Christian leaders—both Indian and British—collaborate peacefully. Although he was not always successful in preventing violence around him, his life provides a steadfast example of the civil disobedience prototype (Constitutional Rights Foundation, 2013). Just as concepts can be abstract or concrete, we can make a distinction between concepts that are functions of our direct experience with the world and those that are more artificial in nature.
Link to Learning
Natural and artificial concepts.
In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations or experiences of snow (Figure 11).
An artificial concept on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.
An event schema , also known as a cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator (Figure 12). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)
Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) (Figure 13).
Remember the elevator? It feels almost impossible to walk in and not face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.
Watch this CrashCourse video to see more examples of concepts and prototypes. You’ll also get a preview on other key topics in cognition, including problem-solving strategies like algorithms and heuristics.
You can view the transcript for “Cognition – How Your Mind Can Amaze and Betray You: Crash Course Psychology #15” here (opens in new window) .
Think It Over
People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.
Problem-Solving Strategies
When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.
A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them. For example, a well-known strategy is trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.
Method | Description | Example |
---|---|---|
Trial and error | Continue trying different solutions until problem is solved | Restarting phone, turning off WiFi, turning off bluetooth in order to determine why your phone is malfunctioning |
Algorithm | Step-by-step problem-solving formula | Instruction manual for installing new software on your computer |
Heuristic | General problem-solving framework | Working backwards; breaking a task into steps |
Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?
A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
- When one is faced with too much information
- When the time to make a decision is limited
- When the decision to be made is unimportant
- When there is access to very little information to use in making the decision
- When an appropriate heuristic happens to come to mind in the same moment
Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.
What problem-solving method could you use to solve Einstein’s famous riddle?
You can view the transcript for “Can you solve “Einstein’s Riddle”? – Dan Van der Vieren” here (opens in new window) .
Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.
Everyday Connections: Solving Puzzles
Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (Figure 14) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.
Here is another popular type of puzzle that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:
Take a look at the “Puzzling Scales” logic puzzle below (Figure 16). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).
Were you able to determine how many marbles are needed to balance the scales in the Puzzling Scales? You need nine. Were you able to solve the other problems above? Here are the answers:
Pitfalls to Problem Solving
Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now. Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.
The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. This bias proves that first impressions do matter and that we tend to look for information to confirm our initial judgments of others.
Watch this video from the Big Think to learn more about the confirmation bias.
You can view the transcript for “Confirmation Bias: Your Brain is So Judgmental” here (opens in new window) .
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . To use a common example, would you guess there are more murders or more suicides in America each year? When asked, most people would guess there are more murders. In truth, there are twice as many suicides as there are murders each year. However, murders seem more common because we hear a lot more about murders on an average day. Unless someone we know or someone famous takes their own life, it does not make the news. Murders, on the other hand, we see in the news every day. This leads to the erroneous assumption that the easier it is to think of instances of something, the more often that thing occurs.
Watch the following video for an example of the availability heuristic.
You can view the transcript for “Availability Heuristic: Are Planes More Dangerous Than Cars?” here (opens in new window) .
Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in Table 2 below.
Bias | Description |
---|---|
Anchoring | Tendency to focus on one particular piece of information when making decisions or problem-solving |
Confirmation | Focuses on information that confirms existing beliefs |
Hindsight | Belief that the event just experienced was predictable |
Representative | Unintentional stereotyping of someone or something |
Availability | Decision is based upon either an available precedent or an example that may be faulty |
Learn more about heuristics and common biases through the article, “ 8 Common Thinking Mistakes Our Brains Make Every Day and How to Prevent Them ” by Belle Beth Cooper.
You can also watch this clever music video explaining these and other cognitive biases.
Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?
- Understand how the use of language develops
- Explain the relationship between language and thinking
Language Development
Language is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.
Components of Language
Language , be it spoken, signed, or written, has specific components: a lexicon and grammar. Lexicon refers to the words of a given language. Thus, lexicon is a language’s vocabulary. Grammar refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.
Words are formed by combining the various phonemes that make up the language. A phoneme (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. Phonemes are combined to form morphemes , which are the smallest units of language that convey some type of meaning (e.g., “I” is both a phoneme and a morpheme). Further, a morpheme is not the same as a word. The main difference is that a morpheme sometimes does not stand alone, but a word, by definition, always stands alone.
We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar. Semantics refers to the process by which we derive meaning from morphemes and words. Syntax refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011).
We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. The flexibility that language provides to relay vastly different types of information is a property that makes language so distinct as a mode of communication among humans.
Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F. Skinner (1957) proposed that language is learned through reinforcement. Noam Chomsky (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).
Children begin to learn about language from a very early age (Table 1). In fact, it appears that this is occurring even before we are born. Newborns show preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).
Stage | Age | Developmental Language and Communication |
---|---|---|
1 | 0–3 months | Reflexive communication |
2 | 3–8 months | Reflexive communication; interest in others |
3 | 8–13 months | Intentional communication; sociability |
4 | 12–18 months | First words |
5 | 18–24 months | Simple sentences of two words |
6 | 2–3 years | Sentences of three or more words |
7 | 3–5 years | Complex sentences; has conversations |
Dig Deeper: The Case of Genie
In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.
Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.
You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).
After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).
Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.
As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).
Language and Thinking
Think about it: the meaning of language.
Think about what you know of other languages; perhaps you even speak multiple languages. Imagine for a moment that your closest friend fluently speaks more than one language. Do you think that friend thinks differently, depending on which language is being spoken? You may know a few words that are not translatable from their original language into English. For example, the Portuguese word saudade originated during the 15th century, when Portuguese sailors left home to explore the seas and travel to Africa or Asia. Those left behind described the emptiness and fondness they felt as saudade (Figure 20) . The word came to express many meanings, including loss, nostalgia, yearning, warm memories, and hope. There is no single word in English that includes all of those emotions in a single description. Do words such as saudade indicate that different languages produce different patterns of thought in people? What do you think??
Language may indeed influence the way that we think, an idea known as linguistic determinism. One recent demonstration of this phenomenon involved differences in the way that English and Mandarin Chinese speakers talk and think about time. English speakers tend to talk about time using terms that describe changes along a horizontal dimension, for example, saying something like “I’m running behind schedule” or “Don’t get ahead of yourself.” While Mandarin Chinese speakers also describe time in horizontal terms, it is not uncommon to also use terms associated with a vertical arrangement. For example, the past might be described as being “up” and the future as being “down.” It turns out that these differences in language translate into differences in performance on cognitive tests designed to measure how quickly an individual can recognize temporal relationships. Specifically, when given a series of tasks with vertical priming, Mandarin Chinese speakers were faster at recognizing temporal relationships between months. Indeed, Boroditsky (2001) sees these results as suggesting that “habits in language encourage habits in thought” (p. 12).
Language does not completely determine our thoughts—our thoughts are far too flexible for that—but habitual uses of language can influence our habit of thought and action. For instance, some linguistic practice seems to be associated even with cultural values and social institution. Pronoun drop is the case in point. Pronouns such as “I” and “you” are used to represent the speaker and listener of a speech in English. In an English sentence, these pronouns cannot be dropped if they are used as the subject of a sentence. So, for instance, “I went to the movie last night” is fine, but “Went to the movie last night” is not in standard English. However, in other languages such as Japanese, pronouns can be, and in fact often are, dropped from sentences. It turned out that people living in those countries where pronoun drop languages are spoken tend to have more collectivistic values (e.g., employees having greater loyalty toward their employers) than those who use non–pronoun drop languages such as English (Kashima & Kashima, 1998). It was argued that the explicit reference to “you” and “I” may remind speakers the distinction between the self and other, and the differentiation between individuals. Such a linguistic practice may act as a constant reminder of the cultural value, which, in turn, may encourage people to perform the linguistic practice.
One group of researchers who wanted to investigate how language influences thought compared how English speakers and the Dani people of Papua New Guinea think and speak about color. The Dani have two words for color: one word for light and one word for dark . In contrast, the English language has 11 color words. Researchers hypothesized that the number of color terms could limit the ways that the Dani people conceptualized color. However, the Dani were able to distinguish colors with the same ability as English speakers, despite having fewer words at their disposal (Berlin & Kay, 1969). A recent review of research aimed at determining how language might affect something like color perception suggests that language can influence perceptual phenomena, especially in the left hemisphere of the brain. You may recall from earlier chapters that the left hemisphere is associated with language for most people. However, the right (less linguistic hemisphere) of the brain is less affected by linguistic influences on perception (Regier & Kay, 2009)
Learn more about language, language acquisition, and especially the connection between language and thought in the following CrashCourse video:
You can view the transcript for “Language: Crash Course Psychology #16” here (opens in new window) .
In this chapter, you learned to
- describe attention
- describe cognition and problem-solving strategies
- describe language acquisition and the role language plays in communication and thought
You learned about non-memory cognitive processes in this chapter. Because each of you reading this is using language in some shape or form, we will end with a quick summary and a video on this topic. Language is a communication system that has both a lexicon and a system of grammar. Language acquisition occurs naturally and effortlessly during the early stages of life, and this acquisition occurs in a predictable sequence for individuals around the world. Language has a strong influence on thought, and the concept of how language may influence cognition remains an area of study and debate in psychology.
In this TED talk, Lera Boroditsky summarizes unique ways that language and culture intersect with some basic cognitive processes. How was your language shaped your thinking?
Abler, W. (2013). Sapir, Harris, and Chomsky in the twentieth century. Cognitive Critique, 7, 29–48.
Aronson, E. (Ed.). (1995). Social cognition. In The social animal (p. 151). New York: W.H. Freeman and Company.
Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge, England: Cambridge University Press.
Bayer, J. B., & Campbell, S. W. (2012). Texting while driving on automatic: Considering the frequency-independent side of habit. Computers in Human Behavior, 28, 2083–2090.
Beilock, S. L., & Carr, T. H. (2001). On the fragility of skilled performance: What governs choking under pressure? Journal of Experimental Psychology: General, 130 , 701–725.
Berlin, B., & Kay, P. (1969). Basic color terms: Their universality and evolution. Berkley: University of California Press.
Blossom, M., & Morgan, J. L. (2006). Does the face say what the mouth says? A study of infants’ sensitivity to visual prosody. In the 30th annual Boston University Conference on Language Development, Somerville, MA.
Boroditsky, L. (2001). Does language shape thought? Mandarin and English speakers’ conceptions of time. Cognitive Psychology, 43, 1–22.
Boroditsky, L. (2011, February). How language shapes thought. Scientific American, 63–65.Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press
Broadbent, D. A. (1958). Perception and communication . London, England: Pergamon Press.
Cairns Regional Council. (n.d.). Cultural greetings. Retrieved from http://www.cairns.qld.gov.au/__data/assets/pdf_file/0007/8953/CulturalGreetingExercise.pdf
Callero, P. L. (1994). From role-playing to role-using: Understanding role as resource. Social Psychology Quarterly, 57, 228–243.
Cherry, E. C. (1953). Experiments on the recognition of speech with one and two ears. Journal of the Acoustical Society of America, 25 , 975–979.
Chomsky, N.(1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press
Corballis, M. C., & Suddendorf, T. (2007). Memory, time, and language. In C. Pasternak (Ed.), What makes us human (pp. 17–36). Oxford, UK: Oneworld Publications.
Curtiss, S. (1981). Dissociations between language and cognition: Cases and implications. Journal of Autism and Developmental Disorders, 11(1), 15–30.
Cyclopedia of Puzzles. (n.d.) Retrieved from http://www.mathpuzzle.com/loyd/
Deutsch, J. A., & Deutsch, D. (1963). Attention: some theoretical considerations. Psychological Review, 70 , 80–90.
Fernández, E. M., & Cairns, H. S. (2011). Fundamentals of psycholinguistics. West Sussex, UK: Wiley-Blackwell.
Fromkin, V., Krashen, S., Curtiss, S., Rigler, D., & Rigler, M. (1974). The development of language in Genie: A case of language acquisition beyond the critical period. Brain and Language, 1, 81–107.
German, T. P., & Barrett, H. C. (2005). Functional fixedness in a technologically sparse culture. Psychological Science, 16, 1–5.
Goldstone, R. L., & Kersten, A. (2003). Concepts and categorization. In A. F. Healy, R. W. Proctor, & I.B. Weiner (Eds.), Handbook of psychology (Volume IV, pp. 599–622). Hoboken, New Jersey: John Wiley & Sons, Inc.
Hirst, W. C., Neisser, U., & Spelke, E. S. (1978). Divided attention. Human Nature, 1 , 54–61.
James, W. (1983). The principles of psychology . Cambridge, MA: Harvard University Press. (Original work published 1890)
Jensen, J. (2011). Phoneme acquisition: Infants and second language learners. The Language Teacher, 35(6), 24–28.
Johnson, J. S., & Newport, E. L. (1989). Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60–99.
Johnston, W. A., & Heinz, S. P. (1978). Flexibility and capacity demands of attention. Journal of Experimental Psychology: General, 107 , 420–435.
Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus, and Giroux.
Lenneberg, E. (1967). Biological foundations of language. New York: Wiley.
Monsell, S. (2003). Task switching. Trends in Cognitive Science, 7 (3), 134–140.
Moray, N. (1959). Attention in dichotic listening: Affective cues and the influence of instructions. Quarterly Journal of Experimental Psychology, 11 , 56–60.
Moskowitz, B. A. (1978). The acquisition of language. Scientific American, 239, 92–108. Petitto, L. A., Holowka, S., Sergio, L. E., Levy, B., & Ostry, D. J. (2004). Baby hands that move to the rhythm of language: Hearing babies acquiring sign languages babble silently on the hands. Cognition, 93, 43–73.
Neyfakh, L. (2013, October 7). “Why you can’t stop checking your phone.” Retrieved from http://www.bostonglobe.com/ideas/2013/10/06/why-you-can-stop-checking-your-phone/rrBJzyBGDAr1YlEH5JQDcM/story.html
Petitto, L. A., Holowka, S., Sergio, L. E., Levy, B., & Ostry, D. J. (2004). Baby hands that move to the rhythm of language: Hearing babies acquiring sign languages babble silently on the hands. Cognition, 93, 43–73.
Pickens, J. (1994). Full-term and preterm infants’ perception of face-voice synchrony. Infant Behavior and Development, 17, 447–455.
Pratkanis, A. (1989). The cognitive representation of attitudes. In A. R. Pratkanis, S. J. Breckler, & A. G. Greenwald (Eds.), Attitude structure and function (pp. 71–98). Hillsdale, NJ: Erlbaum.
Regier, T., & Kay, P. (2009). Language, thought, and color: Whorf was half right. Trends in Cognitive Sciences, 13(10), 439–446.
Rymer, R. (1993). Genie: A Scientific Tragedy. New York: Harper Collins.
Sapir, E. (1964). Culture, language, and personality. Berkley: University of California Press. (Original work published 1941)
Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception, 28 , 1059–1074.
Skinner, B. F. (1957). Verbal behavior. Acton, MA: Copley Publishing Group.
Spelke, E. S., & Cortelyou, A. (1981). Perceptual aspects of social knowing: Looking and listening in infancy. In M.E. Lamb & L.R. Sherrod (Eds.), Infant social cognition: Empirical and theoretical considerations (pp. 61–83). Hillsdale, NJ: Erlbaum.
Spelke, E. S., Hirst, W. C., & Neisser, U. (1976). Skills of divided attention. Cognition, 4 , 215–250.
Strayer, D. L., & Drews, F. A. (2007). Cell-phone induced inattention blindness. Current Directions in Psychological Science, 16 , 128–131.
Strayer, D. L., & Johnston, W. A. (2001). Driven to distraction: Dual-task studies of simulated driving and conversing on a cellular telephone. Psychological Science, 12 , 462–466.
Strayer, D. L., Watson, J. M., & Drews, F. A. (2011) Cognitive distraction while multitasking in the automobile. In Brian Ross (Ed.), The Psychology of Learning and Motivation (Vol. 54, pp. 29–58). Burlington, VT: Academic Press.
Tomasello, M., & Rakoczy, H. (2003). What makes human cognition unique? From individual to shared to collective intentionality. Mind & Language, 18(2), 121–147.
Treisman, A. (1960). Contextual cues in selective listening. Quarterly Journal of Experimental Psychology, 12 , 242–248.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
van Troyer, G. (1994). Linguistic determinism and mutability: The Sapir-Whorf “hypothesis” and intercultural communication. JALT Journal, 2, 163–178.
Watson, J. M., & Strayer, D. L. (2010). Supertaskers: Profiles in extraordinary multitasking ability. Psychonomic Bulletin & Review, 17 , 479–485.
Werker, J. F., & Lalonde, C. E. (1988). Cross-language speech perception: Initial capabilities and developmental change. Developmental Psychology, 24, 672–683.
Werker, J. F., & Tees, R. C. (1984). Cross-language speech perception: Evidence for perceptual reorganization during the first year of life. Infant Behavior and Development, 7, 49–63.
Whorf, B. L. (1956). Language, thought and relativity. Cambridge, MA: MIT Press.
CC original content.
Attention, Thinking and Language. Authored by: Karenna Malavanti Provided by: PressBooks. License: CC BY-SA: Attribution-ShareAlike
CC licensed content, Shared previously
- Why It Matters: Thinking and Intelligence. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/introduction-10/
- Attention. Authored by: Frances Friedrich. Located at NOBA Psychology. License: CC-BY-NC-SA. Retrieved from: Retrieved from http://noba.to/uv9x8df5
- Introduction to Thinking and Intelligence. Authored by : OpenStax College. License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction Located at : https://openstax.org/books/psychology-2e/pages/7-introduction .
- What Is Cognition?. Authored by : OpenStax College. License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction Located at : https://openstax.org/books/psychology-2e/pages/7-1-what-is-cognition .
- A Thinking Man Image. Authored by : Wesley Nitsckie. License : CC BY-SA: Attribution-ShareAlike Located at : https://www.flickr.com/photos/nitsckie/5507777269 .
- What Is Cognition?. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/what-is-cognition/
- Categories and Concepts. Authored by : Gregory Murphy. Provided by : New York University. Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike Located at : http://nobaproject.com/textbooks/wendy-king-introduction-to-psychology-the-full-noba-collection/modules/categories-and-concepts .
- Solving Problems. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/problem-solving/
- Problem-Solving. Authored by : OpenStax College. License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction . Located at : https://openstax.org/books/psychology-2e/pages/7-3-problem-solving .
- Pitfalls to Problem Solving. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/reading-pitfalls-to-problem/
- Introduction to Language. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/outcome-language/
- Language. Authored by : OpenStax College. License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction Located at : https://openstax.org/books/psychology-2e/pages/7-2-language .
- Language. Authored by : geralt. Provided by : Pixabay. License : CC0: No Rights Reserved Located at : https://pixabay.com/en/school-board-languages-blackboard-1063556/ .
- Language and Language Use. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/language-and-language-use/
- Language and Language Use. Authored by : Yoshihisa Kashima. Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike Located at : http://nobaproject.com/textbooks/introduction-to-psychology-the-full-noba-collection/modules/language-and-language-use .
- Language Development. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/language/
- Morpheme. Provided by : Wikipedia. License : CC BY-SA: Attribution-ShareAlike Located at : https://en.wikipedia.org/wiki/Morpheme .
- Language and Thinking. Authored by : Lumen Learning License : CC BY: Attribution Located at : https://pressbooks.online.ucf.edu/lumenpsychology/chapter/reading-language-and-thought/
- Summary. Authored by : OpenStax College. License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction . Located at : https://openstax.org/books/psychology-2e/pages/7-summary .
All rights reserved content
- Cognition: How Your Mind Can Amaze and Betray You – Crash Course Psychology #15. Provided by : CrashCourse. License : All Rights Reserved . License Terms : Standard YouTube License Located at : https://www.youtube.com/watch?v=R-sVnmmw6WY&feature=youtu.be&list=PL8dPuuaLjXtOPRKzVLY0jJY-uHOH9KVU6 .
- Can you solve Einsteinu2019s Riddle? . Authored by : Dan Van der Vieren. Provided by : Ted-Ed. License : Other . License Terms : Standard YouTube License . Located at : https://www.youtube.com/watch?v=1rDVz_Fb6HQ&index=3&list=PLUmyCeox8XCwB8FrEfDQtQZmCc2qYMS5a .
- Language: Crash Course Psychology #16. Authored by : CrashCourse. License : Other . License Terms : Standard YouTube License . Located at : https://www.youtube.com/watch?v=s9shPouRWCs&feature=youtu.be&list=PL8dPuuaLjXtOPRKzVLY0jJY-uHOH9KVU6 .
- How language shapes the way we think Authored by: Lera Boroditsky. Provided by : TED. License : Other . License Terms : Standard YouTube License . Located at : https://youtu.be/RKK7wGAYP6k
thinking, including perception, learning, problem solving, judgment, and memory
field of psychology dedicated to studying every aspect of how people think
a set of objects that can be treated as equivalent in some way
category or grouping of linguistic information, objects, ideas, or life experiences
best representation of a concept
mental groupings that are created “naturally” through your experiences
concept that is defined by a very specific set of characteristics
(plural = schemata) mental construct consisting of a cluster or collection of related concepts
set of expectations that define the behaviors of a person occupying a particular role
set of behaviors that are performed the same way each time; also referred to as a cognitive script
set of behaviors that are performed the same way each time; also referred to as an event schema
method for solving problems
problem-solving strategy in which multiple solutions are attempted until the correct one is found
problem-solving strategy characterized by a specific set of instructions
mental shortcut that saves time when solving a problem
heuristic in which you begin to solve a problem by focusing on the end result
continually using an old solution to a problem without results
inability to see an object as useful for any other use other than the one for which it was intended
faulty heuristic in which you fixate on a single aspect of a problem to find a solution
belief that the event just experienced was predictable, even though it really wasn’t
subset of the population that accurately represents the general population
faulty heuristic in which you make a decision based on information readily available to you
communication system that involves using words to transmit information from one individual to another
Words and expressions.
set of rules that are used to convey meaning through the use of a lexicon
basic sound unit of a given language
smallest unit of language that conveys some type of meaning
process by which we derive meaning from morphemes and words
manner by which words are organized into sentences
extension of a rule that exists in a given language to an exception to the rule
Psychological Science: Understanding Human Behavior Copyright © by Karenna Malavanti is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
Share This Book
6 Thinking and Intelligence
What is the best way to solve a problem? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these and are called cognitive psychologists.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of intelligence? What does an IQ test really measure? These questions and more will be explored in this chapter as you study thinking and intelligence.
In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity before ending with a discussion of how intelligence is measured and how our biology and environments interact to affect intelligence. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Learning Objectives
By the end of this section, you will be able to:
- Describe cognition
- Distinguish concepts and prototypes
- Explain the difference between natural and artificial concepts
- Describe how schemata are organized and constructed
Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition. Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).
Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.
Concepts and Prototypes
The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nerve impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the mind synthesizes information from emotions and memories ( Figure 7.2 ). Emotion and memory are powerful influences on both our thoughts and behaviors.
In order to organize this staggering amount of information, the mind has developed a “file cabinet” of sorts in the mind. The different files stored in the file cabinet are called concepts. Concepts are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.
Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.
Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget’s stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.
Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype is the best example or representation of a concept. For example, what comes to your mind when you think of a dog? Most likely your early experiences with dogs will shape what you imagine. If your first pet was a Golden Retriever, there is a good chance that this would be your prototype for the category of dogs.
Natural and Artificial Concepts
In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations, experiences with snow, or indirect knowledge (such as from films or books) ( Figure 7.3 ).
An artificial concept , on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width), are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.
An event schema , also known as a cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure 7.4 ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)
Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure 7.5 ).
Remember the elevator? It feels almost impossible to walk in and not face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that make refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.
- Define language and demonstrate familiarity with the components of language
- Understand the development of language
- Explain the relationship between language and thinking
Language is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.
Components of Language
Language, be it spoken, signed, or written, has specific components: a lexicon and grammar. Lexicon refers to the words of a given language. Thus, lexicon is a language’s vocabulary. Grammar refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.
Words are formed by combining the various phonemes that make up the language. A phoneme (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. Phonemes are combined to form morphemes , which are the smallest units of language that convey some type of meaning (e.g., “I” is both a phoneme and a morpheme). We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar. Semantics refers to the process by which we derive meaning from morphemes and words. Syntax refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011).
We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. Through our use of words and language, we are able to form, organize, and express ideas, schema, and artificial concepts.
Language Development
Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F. Skinner (1957) proposed that language is learned through reinforcement. Noam Chomsky (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).
Children begin to learn about language from a very early age ( Table 7.1 ). In fact, it appears that this is occurring even before we are born. Newborns show a preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).
Stages of Language and Communication Development | ||
---|---|---|
Stage | Age | Developmental Language and Communication |
1 | 0–3 months | Reflexive communication |
2 | 3–8 months | Reflexive communication; interest in others |
3 | 8–13 months | Intentional communication; sociability |
4 | 12–18 months | First words |
5 | 18–24 months | Simple sentences of two words |
6 | 2–3 years | Sentences of three or more words |
7 | 3–5 years | Complex sentences; has conversations |
DIG DEEPER: The Case of Genie
In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.
Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.
You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).
After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).
Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.
As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).
Language and Thought
When we speak one language, we agree that words are representations of ideas, people, places, and events. The given language that children learn is connected to their culture and surroundings. But can words themselves shape the way we think about things? Psychologists have long investigated the question of whether language shapes thoughts and actions, or whether our thoughts and beliefs shape our language. Two researchers, Edward Sapir and Benjamin Lee Whorf began this investigation in the 1940s. They wanted to understand how the language habits of a community encourage members of that community to interpret language in a particular manner (Sapir, 1941/1964). Sapir and Whorf proposed that language determines thought. For example, in some languages, there are many different words for love. However, in English, we use the word love for all types of love. Does this affect how we think about love depending on the language that we speak (Whorf, 1956)? Researchers have since identified this view as too absolute, pointing out a lack of empiricism behind what Sapir and Whorf proposed (Abler, 2013; Boroditsky, 2011; van Troyer, 1994). Today, psychologists continue to study and debate the relationship between language and thought.
- Describe problem solving strategies
- Define algorithm and heuristic
- Explain some common roadblocks to effective problem solving and decision making
People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe is doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.
Problem-Solving Strategies
When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.
A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is trial and error . The old adage, “If at first, you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.
Problem-Solving Strategies | ||
---|---|---|
Method | Description | Example |
Trial and error | Continue trying different solutions until problem is solved | Restarting phone, turning off WiFi, turning off bluetooth in order to determine why your phone is malfunctioning |
Algorithm | Step-by-step problem-solving formula | Instruction manual for installing new software on your computer |
Heuristic | General problem-solving framework | Working backwards; breaking a task into steps |
Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?
A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of the five conditions is met (Pratkanis, 1989):
- When one is faced with too much information
- When the time to make a decision is limited
- When the decision to be made is unimportant
- When there is access to very little information to use in making the decision
- When an appropriate heuristic happens to come to mind in the same moment
Working backward is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C., and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backward heuristic to plan the events of your day on a regular basis, probably without even thinking about it.
Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or a long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.
EVERYDAY CONNECTION: Solving Puzzles
Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.7 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.
Here is another popular type of puzzle ( Figure 7.8 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:
Take a look at the “Puzzling Scales” logic puzzle below ( Figure 7.9 ). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).
Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.
Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. Duncker (1945) conducted foundational research on functional fixedness. He created an experiment in which participants were given a candle, a book of matches, and a box of thumbtacks. They were instructed to use those items to attach the candle to the wall so that it did not drip wax onto the table below. Participants had to use functional fixedness to solve the problem ( Figure 7.10 ). During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.
Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.
The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in Table 7.3 .
Summary of Decision Biases | |
---|---|
Bias | Description |
Anchoring | Tendency to focus on one particular piece of information when making decisions or problem-solving |
Confirmation | Focuses on information that confirms existing beliefs |
Hindsight | Belief that the event just experienced was predictable |
Representative | Unintentional stereotyping of someone or something |
Availability | Decision is based upon either an available precedent or an example that may be faulty |
Were you able to determine how many marbles are needed to balance the scales in Figure 7.9 ? You need nine. Were you able to solve the problems in Figure 7.7 and Figure 7.8 ? Here are the answers ( Figure 7.11 ).
- Define intelligence
- Explain the triarchic theory of intelligence
- Identify the difference between intelligence theories
- Explain emotional intelligence
- Define creativity
Classifying Intelligence
What exactly is intelligence? The way that researchers have defined the concept of intelligence has been modified many times since the birth of psychology. British psychologist Charles Spearman believed intelligence consisted of one general factor, called g , which could be measured and compared among individuals. Spearman focused on the commonalities among various intellectual abilities and de-emphasized what made each unique. Long before modern psychology developed, however, ancient philosophers, such as Aristotle, held a similar view (Cianciolo & Sternberg, 2004).
Other psychologists believe that instead of a single factor, intelligence is a collection of distinct abilities. In the 1940s, Raymond Cattell proposed a theory of intelligence that divided general intelligence into two components: crystallized intelligence and fluid intelligence (Cattell, 1963). Crystallized intelligence is characterized as acquired knowledge and the ability to retrieve it. When you learn, remember, and recall information, you are using crystallized intelligence. You use crystallized intelligence all the time in your coursework by demonstrating that you have mastered the information covered in the course. Fluid intelligence encompasses the ability to see complex relationships and solve problems. Navigating your way home after being detoured onto an unfamiliar route because of road construction would draw upon your fluid intelligence. Fluid intelligence helps you tackle complex, abstract challenges in your daily life, whereas crystallized intelligence helps you overcome concrete, straightforward problems (Cattell, 1963).
Other theorists and psychologists believe that intelligence should be defined in more practical terms. For example, what types of behaviors help you get ahead in life? Which skills promote success? Think about this for a moment. Being able to recite all 45 presidents of the United States in order is an excellent party trick, but will knowing this make you a better person?
Robert Sternberg developed another theory of intelligence, which he titled the triarchic theory of intelligence because it sees intelligence as comprised of three parts (Sternberg, 1988): practical, creative, and analytical intelligence ( Figure 7.12 ).
Practical intelligence , as proposed by Sternberg, is sometimes compared to “street smarts.” Being practical means you find solutions that work in your everyday life by applying knowledge based on your experiences. This type of intelligence appears to be separate from the traditional understanding of IQ; individuals who score high in practical intelligence may or may not have comparable scores in creative and analytical intelligence (Sternberg, 1988).
Analytical intelligence is closely aligned with academic problem solving and computations. Sternberg says that analytical intelligence is demonstrated by an ability to analyze, evaluate, judge, compare, and contrast. When reading a classic novel for a literature class, for example, it is usually necessary to compare the motives of the main characters of the book or analyze the historical context of the story. In a science course such as anatomy, you must study the processes by which the body uses various minerals in different human systems. In developing an understanding of this topic, you are using analytical intelligence. When solving a challenging math problem, you would apply analytical intelligence to analyze different aspects of the problem and then solve it section by section.
Creative intelligence is marked by inventing or imagining a solution to a problem or situation. Creativity in this realm can include finding a novel solution to an unexpected problem or producing a beautiful work of art or a well-developed short story. Imagine for a moment that you are camping in the woods with some friends and realize that you’ve forgotten your camp coffee pot. The person in your group who figures out a way to successfully brew coffee for everyone would be credited as having higher creative intelligence.
Multiple Intelligences Theory was developed by Howard Gardner, a Harvard psychologist and former student of Erik Erikson. Gardner’s theory, which has been refined for more than 30 years, is a more recent development among theories of intelligence. In Gardner’s theory, each person possesses at least eight intelligences. Among these eight intelligences, a person typically excels in some and falters in others (Gardner, 1983). Table 7.4 describes each type of intelligence.
Multiple Intelligences | ||
---|---|---|
Intelligence Type | Characteristics | Representative Career |
Linguistic intelligence | Perceives different functions of language, different sounds and meanings of words, may easily learn multiple languages | Journalist, novelist, poet, teacher |
Logical-mathematical intelligence | Capable of seeing numerical patterns, strong ability to use reason and logic | Scientist, mathematician |
Musical intelligence | Understands and appreciates rhythm, pitch, and tone; may play multiple instruments or perform as a vocalist | Composer, performer |
Bodily kinesthetic intelligence | High ability to control the movements of the body and use the body to perform various physical tasks | Dancer, athlete, athletic coach, yoga instructor |
Spatial intelligence | Ability to perceive the relationship between objects and how they move in space | Choreographer, sculptor, architect, aviator, sailor |
Interpersonal intelligence | Ability to understand and be sensitive to the various emotional states of others | Counselor, social worker, salesperson |
Intrapersonal intelligence | Ability to access personal feelings and motivations, and use them to direct behavior and reach personal goals | Key component of personal success over time |
Naturalist intelligence | High capacity to appreciate the natural world and interact with the species within it | Biologist, ecologist, environmentalist |
Gardner’s theory is relatively new and needs additional research to better establish empirical support. At the same time, his ideas challenge the traditional idea of intelligence to include a wider variety of abilities, although it has been suggested that Gardner simply relabeled what other theorists called “cognitive styles” as “intelligences” (Morgan, 1996). Furthermore, developing traditional measures of Gardner’s intelligences is extremely difficult (Furnham, 2009; Gardner & Moran, 2006; Klein, 1997).
Gardner’s inter- and intrapersonal intelligences are often combined into a single type: emotional intelligence. Emotional intelligence encompasses the ability to understand the emotions of yourself and others, show empathy, understand social relationships and cues, and regulate your own emotions and respond in culturally appropriate ways (Parker, Saklofske, & Stough, 2009). People with high emotional intelligence typically have well-developed social skills. Some researchers, including Daniel Goleman, the author of Emotional Intelligence: Why It Can Matter More than IQ , argue that emotional intelligence is a better predictor of success than traditional intelligence (Goleman, 1995). However, emotional intelligence has been widely debated, with researchers pointing out inconsistencies in how it is defined and described, as well as questioning results of studies on a subject that is difficult to measure and study empirically (Locke, 2005; Mayer, Salovey, & Caruso, 2004)
The most comprehensive theory of intelligence to date is the Cattell-Horn-Carroll (CHC) theory of cognitive abilities (Schneider & McGrew, 2018). In this theory, abilities are related and arranged in a hierarchy with general abilities at the top, broad abilities in the middle, and narrow (specific) abilities at the bottom. The narrow abilities are the only ones that can be directly measured; however, they are integrated within the other abilities. At the general level is general intelligence. Next, the broad level consists of general abilities such as fluid reasoning, short-term memory, and processing speed. Finally, as the hierarchy continues, the narrow level includes specific forms of cognitive abilities. For example, short-term memory would further break down into memory span and working memory capacity.
Intelligence can also have different meanings and values in different cultures. If you live on a small island, where most people get their food by fishing from boats, it would be important to know how to fish and how to repair a boat. If you were an exceptional angler, your peers would probably consider you intelligent. If you were also skilled at repairing boats, your intelligence might be known across the whole island. Think about your own family’s culture. What values are important for Latinx families? Italian families? In Irish families, hospitality and telling an entertaining story are marks of the culture. If you are a skilled storyteller, other members of Irish culture are likely to consider you intelligent.
Some cultures place a high value on working together as a collective. In these cultures, the importance of the group supersedes the importance of individual achievement. When you visit such a culture, how well you relate to the values of that culture exemplifies your cultural intelligence , sometimes referred to as cultural competence.
Creativity is the ability to generate, create, or discover new ideas, solutions, and possibilities. Very creative people often have intense knowledge about something, work on it for years, look at novel solutions, seek out the advice and help of other experts, and take risks. Although creativity is often associated with the arts, it is actually a vital form of intelligence that drives people in many disciplines to discover something new. Creativity can be found in every area of life, from the way you decorate your residence to a new way of understanding how a cell works.
Creativity is often assessed as a function of one’s ability to engage in divergent thinking . Divergent thinking can be described as thinking “outside the box;” it allows an individual to arrive at unique, multiple solutions to a given problem. In contrast, convergent thinking describes the ability to provide a correct or well-established answer or solution to a problem (Cropley, 2006; Gilford, 1967)
- Explain how intelligence tests are developed
- Describe the history of the use of IQ tests
- Describe the purposes and benefits of intelligence testing
While you’re likely familiar with the term “IQ” and associate it with the idea of intelligence, what does IQ really mean? IQ stands for intelligence quotient and describes a score earned on a test designed to measure intelligence. You’ve already learned that there are many ways psychologists describe intelligence (or more aptly, intelligences). Similarly, IQ tests—the tools designed to measure intelligence—have been the subject of debate throughout their development and use.
When might an IQ test be used? What do we learn from the results, and how might people use this information? While there are certainly many benefits to intelligence testing, it is important to also note the limitations and controversies surrounding these tests. For example, IQ tests have sometimes been used as arguments in support of insidious purposes, such as the eugenics movement (Severson, 2011). The infamous Supreme Court Case, Buck v. Bell , legalized the forced sterilization of some people deemed “feeble-minded” through this type of testing, resulting in about 65,000 sterilizations ( Buck v. Bell , 274 U.S. 200; Ko, 2016). Today, only professionals trained in psychology can administer IQ tests, and the purchase of most tests requires an advanced degree in psychology. Other professionals in the field, such as social workers and psychiatrists, cannot administer IQ tests. In this section, we will explore what intelligence tests measure, how they are scored, and how they were developed.
Measuring Intelligence
It seems that the human understanding of intelligence is somewhat limited when we focus on traditional or academic-type intelligence. How then, can intelligence be measured? And when we measure intelligence, how do we ensure that we capture what we’re really trying to measure (in other words, that IQ tests function as valid measures of intelligence)? In the following paragraphs, we will explore the how intelligence tests were developed and the history of their use.
The IQ test has been synonymous with intelligence for over a century. In the late 1800s, Sir Francis Galton developed the first broad test of intelligence (Flanagan & Kaufman, 2004). Although he was not a psychologist, his contributions to the concepts of intelligence testing are still felt today (Gordon, 1995). Reliable intelligence testing (you may recall from earlier chapters that reliability refers to a test’s ability to produce consistent results) began in earnest during the early 1900s with a researcher named Alfred Binet ( Figure 7.13 ). Binet was asked by the French government to develop an intelligence test to use on children to determine which ones might have difficulty in school; it included many verbally based tasks. American researchers soon realized the value of such testing. Louis Terman, a Stanford professor, modified Binet’s work by standardizing the administration of the test and tested thousands of different-aged children to establish an average score for each age. As a result, the test was normed and standardized, which means that the test was administered consistently to a large enough representative sample of the population that the range of scores resulted in a bell curve (bell curves will be discussed later). Standardization means that the manner of administration, scoring, and interpretation of results is consistent. Norming involves giving a test to a large population so data can be collected comparing groups, such as age groups. The resulting data provide norms, or referential scores, by which to interpret future scores. Norms are not expectations of what a given group should know but a demonstration of what that group does know. Norming and standardizing the test ensures that new scores are reliable. This new version of the test was called the Stanford-Binet Intelligence Scale (Terman, 1916). Remarkably, an updated version of this test is still widely used today.
In 1939, David Wechsler, a psychologist who spent part of his career working with World War I veterans, developed a new IQ test in the United States. Wechsler combined several subtests from other intelligence tests used between 1880 and World War I. These subtests tapped into a variety of verbal and nonverbal skills because Wechsler believed that intelligence encompassed “the global capacity of a person to act purposefully, to think rationally, and to deal effectively with his environment” (Wechsler, 1958, p. 7). He named the test the Wechsler-Bellevue Intelligence Scale (Wechsler, 1981). This combination of subtests became one of the most extensively used intelligence tests in the history of psychology. Although its name was later changed to the Wechsler Adult Intelligence Scale (WAIS) and has been revised several times, the aims of the test remain virtually unchanged since its inception (Boake, 2002). Today, there are three intelligence tests credited to Wechsler, the Wechsler Adult Intelligence Scale-fourth edition (WAIS-IV), the Wechsler Intelligence Scale for Children (WISC-V), and the Wechsler Preschool and Primary Scale of Intelligence—IV (WPPSI-IV) (Wechsler, 2012). These tests are used widely in schools and communities throughout the United States, and they are periodically normed and standardized as a means of recalibration. As a part of the recalibration process, the WISC-V was given to thousands of children across the country, and children taking the test today are compared with their same-age peers ( Figure 7.13 ).
The WISC-V is composed of 14 subtests, which comprise five indices, which then render an IQ score. The five indices are Verbal Comprehension, Visual Spatial, Fluid Reasoning, Working Memory, and Processing Speed. When the test is complete, individuals receive a score for each of the five indices and a Full Scale IQ score. The method of scoring reflects the understanding that intelligence is comprised of multiple abilities in several cognitive realms and focuses on the mental processes that the child used to arrive at his or her answers to each test item.
Interestingly, the periodic recalibrations have led to an interesting observation known as the Flynn effect. Named after James Flynn, who was among the first to describe this trend, the Flynn effect refers to the observation that each generation has a significantly higher IQ than the last. Flynn himself argues, however, that increased IQ scores do not necessarily mean that younger generations are more intelligent per se (Flynn, Shaughnessy, & Fulgham, 2012).
Ultimately, we are still left with the question of how valid intelligence tests are. Certainly, the most modern versions of these tests tap into more than verbal competencies, yet the specific skills that should be assessed in IQ testing, the degree to which any test can truly measure an individual’s intelligence, and the use of the results of IQ tests are still issues of debate (Gresham & Witt, 1997; Flynn, Shaughnessy, & Fulgham, 2012; Richardson, 2002; Schlinger, 2003).
The Bell Curve
The results of intelligence tests follow the bell curve, a graph in the general shape of a bell. When the bell curve is used in psychological testing, the graph demonstrates a normal distribution of a trait, in this case, intelligence, in the human population. Many human traits naturally follow the bell curve. For example, if you lined up all your female schoolmates according to height, it is likely that a large cluster of them would be the average height for an American woman: 5’4”–5’6”. This cluster would fall in the center of the bell curve, representing the average height for American women ( Figure 7.14 ). There would be fewer women who stand closer to 4’11”. The same would be true for women of above-average height: those who stand closer to 5’11”. The trick to finding a bell curve in nature is to use a large sample size. Without a large sample size, it is less likely that the bell curve will represent the wider population. A representative sample is a subset of the population that accurately represents the general population. If, for example, you measured the height of the women in your classroom only, you might not actually have a representative sample. Perhaps the women’s basketball team wanted to take this course together, and they are all in your class. Because basketball players tend to be taller than average, the women in your class may not be a good representative sample of the population of American women. But if your sample included all the women at your school, it is likely that their heights would form a natural bell curve.
The same principles apply to intelligence test scores. Individuals earn a score called an intelligence quotient (IQ). Over the years, different types of IQ tests have evolved, but the way scores are interpreted remains the same. The average IQ score on an IQ test is 100. Standard deviations describe how data are dispersed in a population and give context to large data sets. The bell curve uses the standard deviation to show how all scores are dispersed from the average score ( Figure 7.15 ). In modern IQ testing, one standard deviation is 15 points. So a score of 85 would be described as “one standard deviation below the mean.” How would you describe a score of 115 and a score of 70? Any IQ score that falls within one standard deviation above and below the mean (between 85 and 115) is considered average, and 68% of the population has IQ scores in this range. An IQ score of 130 or above is considered a superior level.
Only 2.2% of the population has an IQ score below 70 (American Psychological Association [APA], 2013). A score of 70 or below indicates significant cognitive delays. When these are combined with major deficits in adaptive functioning, a person is diagnosed with having an intellectual disability (American Association on Intellectual and Developmental Disabilities, 2013). Formerly known as mental retardation, the accepted term now is intellectual disability, and it has four subtypes: mild, moderate, severe, and profound ( Table 7.5 ). The Diagnostic and Statistical Manual of Psychological Disorders lists criteria for each subgroup (APA, 2013).
Characteristics of Cognitive Disorders | ||
---|---|---|
Intellectual Disability Subtype | Percentage of Population with Intellectual Disabilities | Description |
Mild | 85% | 3rd- to 6th-grade skill level in reading, writing, and math; may be employed and live independently |
Moderate | 10% | Basic reading and writing skills; functional self-care skills; requires some oversight |
Severe | 5% | Functional self-care skills; requires oversight of daily environment and activities |
Profound | <1% | May be able to communicate verbally or nonverbally; requires intensive oversight |
On the other end of the intelligence spectrum are those individuals whose IQs fall into the highest ranges. Consistent with the bell curve, about 2% of the population falls into this category. People are considered gifted if they have an IQ score of 130 or higher, or superior intelligence in a particular area. Long ago, popular belief suggested that people of high intelligence were maladjusted. This idea was disproven through a groundbreaking study of gifted children. In 1921, Lewis Terman began a longitudinal study of over 1500 children with IQs over 135 (Terman, 1925). His findings showed that these children became well-educated, successful adults who were, in fact, well-adjusted (Terman & Oden, 1947). Additionally, Terman’s study showed that the subjects were above average in physical build and attractiveness, dispelling an earlier popular notion that highly intelligent people were “weaklings.” Some people with very high IQs elect to join Mensa, an organization dedicated to identifying, researching, and fostering intelligence. Members must have an IQ score in the top 2% of the population, and they may be required to pass other exams in their application to join the group.
DIG DEEPER: What’s in a Name?
In the past, individuals with IQ scores below 70 and significant adaptive and social functioning delays were diagnosed with mental retardation. When this diagnosis was first named, the title held no social stigma. In time, however, the degrading word “retard” sprang from this diagnostic term. “Retard” was frequently used as a taunt, especially among young people, until the words “mentally retarded” and “retard” became an insult. As such, the DSM-5 now labels this diagnosis as “intellectual disability.” Many states once had a Department of Mental Retardation to serve those diagnosed with such cognitive delays, but most have changed their name to the Department of Developmental Disabilities or something similar in language.
Erin Johnson’s younger brother Matthew has Down syndrome. She wrote this piece about what her brother taught her about the meaning of intelligence:
His whole life, learning has been hard. Entirely possible – just different. He has always excelled with technology – typing his thoughts was more effective than writing them or speaking them. Nothing says “leave me alone” quite like a text that reads, “Do Not Call Me Right Now.” He is fully capable of reading books up to about a third-grade level, but he didn’t love it and used to always ask others to read to him. That all changed when his nephew came along, because he willingly reads to him, and it is the most heart-swelling, smile-inducing experience I have ever had the pleasure of witnessing.
When it comes down to it, Matt can learn. He does learn. It just takes longer, and he has to work harder for it, which if we’re being honest, is not a lot of fun. He is extremely gifted in learning things he takes an interest in, and those things often seem a bit “strange” to others. But no matter. It just proves my point – he can learn. That does not mean he will learn at the same pace, or even to the same level. It also, unfortunately, does not mean he will be allotted the same opportunities to learn as many others.
Here’s the scoop. We are all wired with innate abilities to retain and apply our learning and natural curiosities and passions that fuel our desire to learn. But our abilities and curiosities may not be the same.
The world doesn’t work this way though, especially not for my brother and his counterparts. Have him read aloud a book about skunks, and you may not get a whole lot from him. But have him tell you about skunks straight out of his memory, and hold onto your hats. He can hack the school’s iPad system, but he can’t tell you how he did it. He can write out every direction for a drive to our grandparents’ home in Florida, but he can’t drive.
Society is quick to deem him disabled and use demeaning language like the r-word to describe him, but in reality, we haven’t necessarily given him opportunities to showcase the learning he can do. In my case, I can escape the need to memorize how to change the oil in my car without anyone assuming I can’t do it, or calling me names when they find out I can’t. But Matthew can’t get through a day at his job without someone assuming he needs help. He is bright. Brighter than most anyone would assume. Maybe we need to redefine what is smart.
My brother doesn’t fit in the narrow schema of intelligence that is accepted in our society. But intelligence is far more than being able to solve 525 x 62 or properly introduce yourself to another. Why can’t we assume the intelligence of someone who can recite all of a character’s lines in a movie or remember my birthday a year after I told him/her a single time? Why is it we allow a person’s diagnosis or appearance to make us not just wonder if, but entirely doubt that they are capable? Maybe we need to cut away the sides of the box we have created for people so everyone can fit.
My brother can learn. It may not be what you know. It may be knowledge you would deem unimportant. It may not follow a traditional learning trajectory. But the fact remains – he can learn. Everyone can learn. And even though it is harder for him and harder for others still, he is not a “retard.” Nobody is.
When you use the r-word, you are insinuating that an individual, whether someone with a disability or not, is unintelligent, foolish, and purposeless. This in turn tells a person with a disability that they too are unintelligent, foolish, and purposeless. Because the word was historically used to describe individuals with disabilities and twisted from its original meaning to fit a cruel new context, it is forevermore associated with people like my brother. No matter how a person looks or learns or behaves, the r-word is never a fitting term. It’s time we waved it goodbye.
Why Measure Intelligence?
The value of IQ testing is most evident in educational or clinical settings. Children who seem to be experiencing learning difficulties or severe behavioral problems can be tested to ascertain whether the child’s difficulties can be partly attributed to an IQ score that is significantly different from the mean for her age group. Without IQ testing—or another measure of intelligence—children and adults needing extra support might not be identified effectively. In addition, IQ testing is used in courts to determine whether a defendant has special or extenuating circumstances that preclude him from participating in some way in a trial. People also use IQ testing results to seek disability benefits from the Social Security Administration.
- Describe how genetics and environment affect intelligence
- Explain the relationship between IQ scores and socioeconomic status
- Describe the difference between a learning disability and a developmental disorder
High Intelligence: Nature or Nurture?
Where does high intelligence come from? Some researchers believe that intelligence is a trait inherited from a person’s parents. Scientists who research this topic typically use twin studies to determine the heritability of intelligence. The Minnesota Study of Twins Reared Apart is one of the most well-known twin studies. In this investigation, researchers found that identical twins raised together and identical twins raised apart exhibit a higher correlation between their IQ scores than siblings or fraternal twins raised together (Bouchard, Lykken, McGue, Segal, & Tellegen, 1990). The findings from this study reveal a genetic component to intelligence ( Figure 7.15 ). At the same time, other psychologists believe that intelligence is shaped by a child’s developmental environment. If parents were to provide their children with intellectual stimuli from before they are born, it is likely that they would absorb the benefits of that stimulation, and it would be reflected in intelligence levels.
The reality is that aspects of each idea are probably correct. In fact, one study suggests that although genetics seem to be in control of the level of intelligence, the environmental influences provide both stability and change to trigger manifestation of cognitive abilities (Bartels, Rietveld, Van Baal, & Boomsma, 2002). Certainly, there are behaviors that support the development of intelligence, but the genetic component of high intelligence should not be ignored. As with all heritable traits, however, it is not always possible to isolate how and when high intelligence is passed on to the next generation.
Range of Reaction is the theory that each person responds to the environment in a unique way based on his or her genetic makeup. According to this idea, your genetic potential is a fixed quantity, but whether you reach your full intellectual potential is dependent upon the environmental stimulation you experience, especially in childhood. Think about this scenario: A couple adopts a child who has average genetic intellectual potential. They raise her in an extremely stimulating environment. What will happen to the couple’s new daughter? It is likely that the stimulating environment will improve her intellectual outcomes over the course of her life. But what happens if this experiment is reversed? If a child with an extremely strong genetic background is placed in an environment that does not stimulate him: What happens? Interestingly, according to a longitudinal study of highly gifted individuals, it was found that “the two extremes of optimal and pathological experience are both represented disproportionately in the backgrounds of creative individuals”; however, those who experienced supportive family environments were more likely to report being happy (Csikszentmihalyi & Csikszentmihalyi, 1993, p. 187).
Another challenge to determining the origins of high intelligence is the confounding nature of our human social structures. It is troubling to note that some ethnic groups perform better on IQ tests than others—and it is likely that the results do not have much to do with the quality of each ethnic group’s intellect. The same is true for socioeconomic status. Children who live in poverty experience more pervasive, daily stress than children who do not worry about the basic needs of safety, shelter, and food. These worries can negatively affect how the brain functions and develops, causing a dip in IQ scores. Mark Kishiyama and his colleagues determined that children living in poverty demonstrated reduced prefrontal brain functioning comparable to children with damage to the lateral prefrontal cortex (Kishyama, Boyce, Jimenez, Perry, & Knight, 2009).
The debate around the foundations and influences on intelligence exploded in 1969 when an educational psychologist named Arthur Jensen published the article “How Much Can We Boost I.Q. and Achievement” in the Harvard Educational Review . Jensen had administered IQ tests to diverse groups of students, and his results led him to the conclusion that IQ is determined by genetics. He also posited that intelligence was made up of two types of abilities: Level I and Level II. In his theory, Level I is responsible for rote memorization, whereas Level II is responsible for conceptual and analytical abilities. According to his findings, Level I remained consistent among the human race. Level II, however, exhibited differences among ethnic groups (Modgil & Routledge, 1987). Jensen’s most controversial conclusion was that Level II intelligence is prevalent among Asians, then Caucasians, then African Americans. Robert Williams was among those who called out racial bias in Jensen’s results (Williams, 1970).
Obviously, Jensen’s interpretation of his own data caused an intense response in a nation that continued to grapple with the effects of racism (Fox, 2012). However, Jensen’s ideas were not solitary or unique; rather, they represented one of many examples of psychologists asserting racial differences in IQ and cognitive ability. In fact, Rushton and Jensen (2005) reviewed three decades worth of research on the relationship between race and cognitive ability. Jensen’s belief in the inherited nature of intelligence and the validity of the IQ test to be the truest measure of intelligence are at the core of his conclusions. If, however, you believe that intelligence is more than Levels I and II, or that IQ tests do not control for socioeconomic and cultural differences among people, then perhaps you can dismiss Jensen’s conclusions as a single window that looks out on the complicated and varied landscape of human intelligence.
In a related story, parents of African American students filed a case against the State of California in 1979, because they believed that the testing method used to identify students with learning disabilities was culturally unfair as the tests were normed and standardized using white children ( Larry P. v. Riles ). The testing method used by the state disproportionately identified African American children as mentally retarded. This resulted in many students being incorrectly classified as “mentally retarded.”
What are Learning Disabilities?
Learning disabilities are cognitive disorders that affect different areas of cognition, particularly language or reading. It should be pointed out that learning disabilities are not the same thing as intellectual disabilities. Learning disabilities are considered specific neurological impairments rather than global intellectual or developmental disabilities. A person with a language disability has difficulty understanding or using spoken language, whereas someone with a reading disability, such as dyslexia, has difficulty processing what he or she is reading.
Often, learning disabilities are not recognized until a child reaches school age. One confounding aspect of learning disabilities is that they most often affect children with average to above-average intelligence. In other words, the disability is specific to a particular area and not a measure of overall intellectual ability. At the same time, learning disabilities tend to exhibit comorbidity with other disorders, like attention-deficit hyperactivity disorder (ADHD). Anywhere between 30–70% of individuals with diagnosed cases of ADHD also have some sort of learning disability (Riccio, Gonzales, & Hynd, 1994). Let’s take a look at three examples of common learning disabilities: dysgraphia, dyslexia, and dyscalculia.
Children with dysgraphia have a learning disability that results in a struggle to write legibly. The physical task of writing with a pen and paper is extremely challenging for the person. These children often have extreme difficulty putting their thoughts down on paper (Smits-Engelsman & Van Galen, 1997). This difficulty is inconsistent with a person’s IQ. That is, based on the child’s IQ and/or abilities in other areas, a child with dysgraphia should be able to write, but can’t. Children with dysgraphia may also have problems with spatial abilities.
Students with dysgraphia need academic accommodations to help them succeed in school. These accommodations can provide students with alternative assessment opportunities to demonstrate what they know (Barton, 2003). For example, a student with dysgraphia might be permitted to take an oral exam rather than a traditional paper-and-pencil test. Treatment is usually provided by an occupational therapist, although there is some question as to how effective such treatment is (Zwicker, 2005).
Dyslexia is the most common learning disability in children. An individual with dyslexia exhibits an inability to correctly process letters. The neurological mechanism for sound processing does not work properly in someone with dyslexia. As a result, dyslexic children may not understand sound-letter correspondence. A child with dyslexia may mix up letters within words and sentences—letter reversals, such as those shown in Figure 7.17 , are a hallmark of this learning disability—or skip whole words while reading. A dyslexic child may have difficulty spelling words correctly while writing. Because of the disordered way that the brain processes letters and sounds, learning to read is a frustrating experience. Some dyslexic individuals cope by memorizing the shapes of most words, but they never actually learn to read (Berninger, 2008).
Dyscalculia
Dyscalculia is difficulty in learning or comprehending arithmetic. This learning disability is often first evident when children exhibit difficulty discerning how many objects are in a small group without counting them. Other symptoms may include struggling to memorize math facts, organize numbers, or fully differentiate between numerals, math symbols, and written numbers (such as “3” and “three”).
Additional Supplemental Resources
- Use Google’s QuickDraw web app on your phone to quickly draw 5 things for Google’s artificially intelligent neural net. When you are done, the app will show you what it thought each of the drawings was. How does this relate to the psychological idea of concepts, prototypes, and schemas? Check out here. Works best in Chrome if used in a web browser
- This article lists information about a variety of different topics relating to speech development, including how speech develops and what research is currently being done regarding speech development.
- The Human intelligence site includes biographical profiles of people who have influenced the development of intelligence theory and testing, in-depth articles exploring current controversies related to human intelligence, and resources for teachers.
- In 2000, psychologists Sheena Iyengar and Mark Lepper from Columbia and Stanford University published a study about the paradox of choice. This is the original journal article.
- Mensa , the high IQ society, provides a forum for intellectual exchange among its members. There are members in more than 100 countries around the world. Anyone with an IQ in the top 2% of the population can join.
- This test developed in the 1950s is used to refer to some kinds of behavioral tests for the presence of mind, or thought, or intelligence in putatively minded entities such as machines.
- Your central “Hub” of information and products created for the network of Parent Centers serving families of children with disabilities.
- How have average IQ levels changed over time? Hear James Flynn discuss the “Flynn Effect” in this Ted Talk. Closed captioning available.
- We all want customized experiences and products — but when faced with 700 options, consumers freeze up. With fascinating new research, Sheena Iyengar demonstrates how businesses (and others) can improve the experience of choosing. This is the same researcher that is featured in your midterm exam.
- What does an IQ Score distribution look like? Where do most people fall on an IQ Score distribution? Find out more in this video. Closed captioning available.
- How do we solve problems? How can data help us to do this? Follow Amy Webb’s story of how she used algorithms to help her find her way to true love. Closed captioning available.
- In this Ted-Ed video, explore some of the ways in which animals communicate, and determine whether or not this communication qualifies as language. A variety of discussion and assessment questions are included with the video (free registration is required to access the questions). Closed captioning available.
- Watch this Ted-Ed video to learn more about the benefits of speaking multiple languages, including how bilingualism helps the brain to process information, strengthens the brain, and keeps the speaker more engaged in their world. A variety of discussion and assessment questions are included with the video (free registration is required to access the questions). Closed captioning available.
- This video is on how your mind can amaze and betray you includes information on topics such as concepts, prototypes, problem-solving and mistakes in thinking. Closed captioning available.
- This video on language includes information on topics such as the development of language, language theories, and brain areas involved in language, as well as language disorders. Closed captioning available.
- This video on the controversy of intelligence includes information on topics such as theories of intelligence, emotional intelligence, and measuring intelligence. Closed captioning available.
- This video on the brains vs. bias includes information on topics such as intelligence testing, testing bias, and stereotype threat. Closed captioning available.
Access for free at https://openstax.org/books/psychology-2e/pages/1-introduction
Introduction to Psychology Copyright © 2020 by Julie Lazzara is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
Share This Book
- Get started with computers
- Learn Microsoft Office
- Apply for a job
- Improve my work skills
- Design nice-looking docs
- Getting Started
- Smartphones & Tablets
- Typing Tutorial
- Online Learning
- Basic Internet Skills
- Online Safety
- Social Media
- Zoom Basics
- Google Docs
- Google Sheets
- Career Planning
- Resume Writing
- Cover Letters
- Job Search and Networking
- Business Communication
- Entrepreneurship 101
- Careers without College
- Job Hunt for Today
- 3D Printing
- Freelancing 101
- Personal Finance
- Sharing Economy
- Decision-Making
- Graphic Design
- Photography
- Image Editing
- Learning WordPress
- Language Learning
- Critical Thinking
- For Educators
- Translations
- Staff Picks
- English expand_more expand_less
Critical Thinking and Decision-Making - What is Critical Thinking?
Critical thinking and decision-making -, what is critical thinking, critical thinking and decision-making what is critical thinking.
Critical Thinking and Decision-Making: What is Critical Thinking?
Lesson 1: what is critical thinking, what is critical thinking.
Critical thinking is a term that gets thrown around a lot. You've probably heard it used often throughout the years whether it was in school, at work, or in everyday conversation. But when you stop to think about it, what exactly is critical thinking and how do you do it ?
Watch the video below to learn more about critical thinking.
Simply put, critical thinking is the act of deliberately analyzing information so that you can make better judgements and decisions . It involves using things like logic, reasoning, and creativity, to draw conclusions and generally understand things better.
This may sound like a pretty broad definition, and that's because critical thinking is a broad skill that can be applied to so many different situations. You can use it to prepare for a job interview, manage your time better, make decisions about purchasing things, and so much more.
The process
As humans, we are constantly thinking . It's something we can't turn off. But not all of it is critical thinking. No one thinks critically 100% of the time... that would be pretty exhausting! Instead, it's an intentional process , something that we consciously use when we're presented with difficult problems or important decisions.
Improving your critical thinking
In order to become a better critical thinker, it's important to ask questions when you're presented with a problem or decision, before jumping to any conclusions. You can start with simple ones like What do I currently know? and How do I know this? These can help to give you a better idea of what you're working with and, in some cases, simplify more complex issues.
Real-world applications
Let's take a look at how we can use critical thinking to evaluate online information . Say a friend of yours posts a news article on social media and you're drawn to its headline. If you were to use your everyday automatic thinking, you might accept it as fact and move on. But if you were thinking critically, you would first analyze the available information and ask some questions :
- What's the source of this article?
- Is the headline potentially misleading?
- What are my friend's general beliefs?
- Do their beliefs inform why they might have shared this?
After analyzing all of this information, you can draw a conclusion about whether or not you think the article is trustworthy.
Critical thinking has a wide range of real-world applications . It can help you to make better decisions, become more hireable, and generally better understand the world around you.
/en/problem-solving-and-decision-making/why-is-it-so-hard-to-make-decisions/content/
Chapter 7: Thinking and Intelligence
Learning objectives.
- Distinguish between concepts and prototypes
- Explain the difference between natural and artificial concepts
- Describe problem-solving strategies, including algorithms and heuristics
- Explain some common roadblocks to effective problem solving, including choice blindness
- Explain the triarchic theory of intelligence
- Explain the multiple intelligences theory
- Define creativity, divergent, and convergent thinking
- Describe the development of IQ tests, their purposes, and benefits
- Explain the bell curve, and how IQ is measured
- Describe how genetics and the environment affect intelligence
- Describe common learning disabilities
Figure 1 . Thinking is an important part of our human experience, and one that has captivated people for centuries. Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the 20th-century sculpture The Thinker by August Rodin, and Shi Ke’s 10th-century painting Huike Thinking all reflect the fascination with the process of human thought. (credit “middle”: modification of work by Jason Rogers; credit “right”: modification of work by Tang Zu-Ming)
Why is it so difficult to break habits—like reaching for your ringing phone even when you shouldn’t, such as when you’re driving? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these.
Cognitive psychologists also study intelligence. What is intelligence, and how does it vary from person to person? Are “street smarts” a kind of intelligence, and if so, how do they relate to other types of intelligence? What does an IQ test really measure? These questions and more will be explored in this module as you study thinking and intelligence.
As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity, intelligence testing, and how our biology and environments interact to affect intelligence. After finishing this module, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.
Privacy Policy
What is Problem Solving? (Steps, Techniques, Examples)
By Status.net Editorial Team on May 7, 2023 — 5 minutes to read
What Is Problem Solving?
Definition and importance.
Problem solving is the process of finding solutions to obstacles or challenges you encounter in your life or work. It is a crucial skill that allows you to tackle complex situations, adapt to changes, and overcome difficulties with ease. Mastering this ability will contribute to both your personal and professional growth, leading to more successful outcomes and better decision-making.
Problem-Solving Steps
The problem-solving process typically includes the following steps:
- Identify the issue : Recognize the problem that needs to be solved.
- Analyze the situation : Examine the issue in depth, gather all relevant information, and consider any limitations or constraints that may be present.
- Generate potential solutions : Brainstorm a list of possible solutions to the issue, without immediately judging or evaluating them.
- Evaluate options : Weigh the pros and cons of each potential solution, considering factors such as feasibility, effectiveness, and potential risks.
- Select the best solution : Choose the option that best addresses the problem and aligns with your objectives.
- Implement the solution : Put the selected solution into action and monitor the results to ensure it resolves the issue.
- Review and learn : Reflect on the problem-solving process, identify any improvements or adjustments that can be made, and apply these learnings to future situations.
Defining the Problem
To start tackling a problem, first, identify and understand it. Analyzing the issue thoroughly helps to clarify its scope and nature. Ask questions to gather information and consider the problem from various angles. Some strategies to define the problem include:
- Brainstorming with others
- Asking the 5 Ws and 1 H (Who, What, When, Where, Why, and How)
- Analyzing cause and effect
- Creating a problem statement
Generating Solutions
Once the problem is clearly understood, brainstorm possible solutions. Think creatively and keep an open mind, as well as considering lessons from past experiences. Consider:
- Creating a list of potential ideas to solve the problem
- Grouping and categorizing similar solutions
- Prioritizing potential solutions based on feasibility, cost, and resources required
- Involving others to share diverse opinions and inputs
Evaluating and Selecting Solutions
Evaluate each potential solution, weighing its pros and cons. To facilitate decision-making, use techniques such as:
- SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)
- Decision-making matrices
- Pros and cons lists
- Risk assessments
After evaluating, choose the most suitable solution based on effectiveness, cost, and time constraints.
Implementing and Monitoring the Solution
Implement the chosen solution and monitor its progress. Key actions include:
- Communicating the solution to relevant parties
- Setting timelines and milestones
- Assigning tasks and responsibilities
- Monitoring the solution and making adjustments as necessary
- Evaluating the effectiveness of the solution after implementation
Utilize feedback from stakeholders and consider potential improvements. Remember that problem-solving is an ongoing process that can always be refined and enhanced.
Problem-Solving Techniques
During each step, you may find it helpful to utilize various problem-solving techniques, such as:
- Brainstorming : A free-flowing, open-minded session where ideas are generated and listed without judgment, to encourage creativity and innovative thinking.
- Root cause analysis : A method that explores the underlying causes of a problem to find the most effective solution rather than addressing superficial symptoms.
- SWOT analysis : A tool used to evaluate the strengths, weaknesses, opportunities, and threats related to a problem or decision, providing a comprehensive view of the situation.
- Mind mapping : A visual technique that uses diagrams to organize and connect ideas, helping to identify patterns, relationships, and possible solutions.
Brainstorming
When facing a problem, start by conducting a brainstorming session. Gather your team and encourage an open discussion where everyone contributes ideas, no matter how outlandish they may seem. This helps you:
- Generate a diverse range of solutions
- Encourage all team members to participate
- Foster creative thinking
When brainstorming, remember to:
- Reserve judgment until the session is over
- Encourage wild ideas
- Combine and improve upon ideas
Root Cause Analysis
For effective problem-solving, identifying the root cause of the issue at hand is crucial. Try these methods:
- 5 Whys : Ask “why” five times to get to the underlying cause.
- Fishbone Diagram : Create a diagram representing the problem and break it down into categories of potential causes.
- Pareto Analysis : Determine the few most significant causes underlying the majority of problems.
SWOT Analysis
SWOT analysis helps you examine the Strengths, Weaknesses, Opportunities, and Threats related to your problem. To perform a SWOT analysis:
- List your problem’s strengths, such as relevant resources or strong partnerships.
- Identify its weaknesses, such as knowledge gaps or limited resources.
- Explore opportunities, like trends or new technologies, that could help solve the problem.
- Recognize potential threats, like competition or regulatory barriers.
SWOT analysis aids in understanding the internal and external factors affecting the problem, which can help guide your solution.
Mind Mapping
A mind map is a visual representation of your problem and potential solutions. It enables you to organize information in a structured and intuitive manner. To create a mind map:
- Write the problem in the center of a blank page.
- Draw branches from the central problem to related sub-problems or contributing factors.
- Add more branches to represent potential solutions or further ideas.
Mind mapping allows you to visually see connections between ideas and promotes creativity in problem-solving.
Examples of Problem Solving in Various Contexts
In the business world, you might encounter problems related to finances, operations, or communication. Applying problem-solving skills in these situations could look like:
- Identifying areas of improvement in your company’s financial performance and implementing cost-saving measures
- Resolving internal conflicts among team members by listening and understanding different perspectives, then proposing and negotiating solutions
- Streamlining a process for better productivity by removing redundancies, automating tasks, or re-allocating resources
In educational contexts, problem-solving can be seen in various aspects, such as:
- Addressing a gap in students’ understanding by employing diverse teaching methods to cater to different learning styles
- Developing a strategy for successful time management to balance academic responsibilities and extracurricular activities
- Seeking resources and support to provide equal opportunities for learners with special needs or disabilities
Everyday life is full of challenges that require problem-solving skills. Some examples include:
- Overcoming a personal obstacle, such as improving your fitness level, by establishing achievable goals, measuring progress, and adjusting your approach accordingly
- Navigating a new environment or city by researching your surroundings, asking for directions, or using technology like GPS to guide you
- Dealing with a sudden change, like a change in your work schedule, by assessing the situation, identifying potential impacts, and adapting your plans to accommodate the change.
- How to Resolve Employee Conflict at Work [Steps, Tips, Examples]
- How to Write Inspiring Core Values? 5 Steps with Examples
- 30 Employee Feedback Examples (Positive & Negative)
- Bipolar Disorder
- Therapy Center
- When To See a Therapist
- Types of Therapy
- Best Online Therapy
- Best Couples Therapy
- Best Family Therapy
- Managing Stress
- Sleep and Dreaming
- Understanding Emotions
- Self-Improvement
- Healthy Relationships
- Student Resources
- Personality Types
- Guided Meditations
- Verywell Mind Insights
- 2024 Verywell Mind 25
- Mental Health in the Classroom
- Editorial Process
- Meet Our Review Board
- Crisis Support
What Does 'Cognitive' Mean in Psychology?
How People Think and What's Involved in Cognitive Processes
Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Daniel B. Block, MD, is an award-winning, board-certified psychiatrist who operates a private practice in Pennsylvania.
Verywell / Laura Porter
- Improving Cognitive Skills
Frequently Asked Questions
'Cognitive' is a term used in psychology to describe anything related to thinking, learning, and understanding. So when you hear people talk about cognitive skills or processes, they are referring to different aspects of how the brain works—things like remembering information, learning new things, paying attention, and processing all of the information you encounter each day.
Cognitive abilities are something you use each and every day. For example, when you are learning a new instrument, you are using your cognitive skills to learn the basics of music theory, pick up melodies, learn the notes, and put that information together to produce music.
'Cognitive' refers to the mental processes involved in gaining knowledge and comprehension. Some of the many different cognitive processes include thinking, knowing, remembering, judging, and problem-solving .
These are higher-level brain functions that encompass language, imagination, perception, and planning. Cognitive psychology is the field of psychology that investigates how people think and the processes involved in cognition.
At a Glance
Cognitive psychology seeks to understand all of the mental processes involved in human thought and behavior. It focuses on cognitive processes such as decision-making, problem-solving, attention, memory, learning, and more. Keep reading to learn more about different types of cognitive processes, factors that can affect cognition, and the different uses for these cognitive processes.
Types of Cognitive Processes
There are many different types of cognitive processes. They include:
Attention is a cognitive process that allows people to focus on a specific environmental stimulus. Attention is an important cognitive ability because it allows us to focus on the information we need, while also filtering out irrelevant distractions.
Language and language development are cognitive processes that involve the ability to understand and express thoughts through spoken and written words. This allows us to communicate with others, including conveying our own thoughts and learning about others. It also plays an important role in thought.
Learning requires cognitive processes involved in taking in new things, synthesizing information, and integrating it with prior knowledge. Cognitive psychologists often study the mental processes that involved in processing, comprehending, and remembering information.
Memory is an important cognitive process that allows people to encode, store, and retrieve information. It is a critical component in the learning process and allows people to retain knowledge about the world and their personal histories.
Perception is a cognitive process that allows people to take in information through their senses, then utilize this information to respond and interact with the world.
Thought is an essential part of every cognitive process. It allows people to engage in decision-making , problem-solving, and higher reasoning.
Hot Cognition vs. Cold Cognition
Some split cognition into two categories: hot and cold. Hot cognition refers to mental processes in which emotion plays a role, such as reward-based learning . Conversely, cold cognition refers to mental processes that don't involve feelings or emotions, such as working memory .
What is an example of cognition?
Cognition includes all of the conscious and unconscious processes involved in thinking, perceiving, and reasoning. Examples of cognition include paying attention to something in the environment, learning something new, making decisions, processing language, sensing and perceiving environmental stimuli, solving problems, and using memory.
History of the Study of Cognition
The study of how humans think dates back to the time of ancient Greek philosophers Plato and Aristotle.
Philosophical Origins
Plato's approach to the study of the mind suggested that people understand the world by first identifying basic principles buried deep inside themselves, then using rational thought to create knowledge. This viewpoint was later advocated by philosophers such as Rene Descartes and linguist Noam Chomsky. It is often referred to as rationalism.
Aristotle, on the other hand, believed that people acquire knowledge through their observations of the world around them. Later thinkers such as John Locke and B.F. Skinner also advocated this point of view, which is often referred to as empiricism.
Early Psychology
During the earliest days of psychology—and for the first half of the 20th century—psychology was largely dominated by psychoanalysis , behaviorism , and humanism .
Eventually, a formal field of study devoted solely to the study of cognition emerged as part of the "cognitive revolution" of the 1960s. This field is known as cognitive psychology.
The Emergence of Cognitive Psychology
One of the earliest definitions of cognition was presented in the first textbook on cognitive psychology, which was published in 1967. According to Ulric Neisser, a psychologist and the book's author, cognition is "those processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used."
What Can Affect Cognitive Processes?
It is important to remember that these cognitive processes are complex and often imperfect. Some of the factors that can affect or influence cognition include:
Research indicates that as we age, our cognitive function tends to decline. Age-related cognitive changes include processing things more slowly, finding it harder to recall past events, and a failure to remember information that was once known (such as how to solve a particular math equation or historical information).
Attention Issues
Selective attention is a limited resource, so there are a number of things that can make it difficult to focus on everything in your environment. Attentional blink , for example, happens when you are so focused on one thing that you completely miss something else happening right in front of you.
Cognitive Biases
Cognitive biases are systematic errors in thinking related to how people process and interpret information about the world. Confirmation bias is one common example that involves only paying attention to information that aligns with your existing beliefs while ignoring evidence that doesn't support your views.
Some studies have connected cognitive function with certain genes. For example, a 2020 study published in Brain Communications found that a person's level of brain-derived neurotrophic factor (BDNF), which is 30% determined by heritability, can impact the rate of brain neurodegeneration, a condition that ultimately impacts cognitive function.
Memory Limitations
Short-term memory is surprisingly brief, typically lasting just 20 to 30 seconds, whereas long-term memory can be stable and enduring, with memories lasting years and even decades. Memory can also be fragile and fallible. Sometimes we forget and other times we are subject to misinformation effects that may even lead to the formation of false memories .
Uses for Cognitive Processes
Cognitive processes affect every aspect of life, from school to work to relationships. Some specific uses for these processes include the following.
Learning New Things
Learning requires being able to take in new information, form new memories, and make connections with other things that you already know. Researchers and educators use their knowledge of these cognitive processes to create instructive materials to help people learn new concepts .
Forming Memories
Memory is a major topic of interest in the field of cognitive psychology. How we remember, what we remember, and what we forget reveal a great deal about how cognitive processes operate.
While people often think of memory as being much like a video camera—carefully recording, cataloging, and storing life events away for later recall—research has found that memory is much more complex.
Making Decisions
Whenever people make any type of a decision, it involves making judgments about things they have processed. This might involve comparing new information to prior knowledge, integrating new information into existing ideas, or even replacing old knowledge with new knowledge before making a choice.
Impact of Cognition
Our cognitive processes have a wide-ranging impact that influences everything from our daily life to our overall health.
Perceiving the World
As you take in sensations from the world around you, the information that you see, hear, taste, touch, and smell must first be transformed into signals that the brain can understand. The perceptual process allows you to take in this sensory information and convert it into a signal that your brain can recognize and act upon.
Forming Impressions
The world is full of an endless number of sensory experiences . To make meaning out of all this incoming information, it is important for the brain to be able to capture the fundamentals. Events are reduced to only the critical concepts and ideas that we need.
Filling in the Gaps
In addition to reducing information to make it more memorable and understandable, people also elaborate on these memories as they reconstruct them. In some cases, this elaboration happens when people are struggling to remember something . When the information cannot be recalled, the brain sometimes fills in the missing data with whatever seems to fit.
Interacting With the World
Cognition involves not only the things that go on inside our heads but also how these thoughts and mental processes influence our actions. Our attention to the world around us, memories of past events, understanding of language, judgments about how the world works, and abilities to solve problems all contribute to how we behave and interact with our surrounding environment.
Tips for Improving Cognitive Skills
Cognitive processes are influenced by a range of factors, including genetics and experiences. While you cannot change your genes or age, there are things that you can do to protect and maximize your cognitive abilities:
- Stay healthy . Lifestyle factors such as eating a nutritious diet and getting regular exercise can have a positive effect on cognitive functioning.
- Think critically . Question your assumptions and ask questions about your thoughts, beliefs, and conclusions.
- Stay curious and keep learning . A great way to flex your cognitive abilities is to keep challenging yourself to learn more about the world.
- Skip multitasking . While it might seem like doing several things at once would help you get done faster, research has shown it actually decreases both productivity and work quality.
In psychology, the term 'cognitive' refers to all of the different mental events involved in thinking, learning, and comprehending. Cognitive processes such as learning, attention, perception, and memory are important parts of the human experience. Understanding how they function can provide insight into normal human thought and behavior and how different cognitive conditions might create problems and impairments.
Thinking is an important component, but cognition also encompasses unconscious and perceptual processes as well. In addition to thinking, cognition involves language, attention, learning, memory, and perception.
People utilize cognitive skills to think, learn, recall, and reason. Five important cognitive skills include short-term memory, logic, processing speed, attention, and spatial recognition.
American Psychological Association. Cognition .
Ezebuilo HC. Descartes, Leibniz and Spinoza: A brief survey of rationalism . J App Philos . 2020;18(6):95-118. doi:10.13140/RG.2.2.19692.39043
Sgarbi M. The Aristotelian Tradition and the Rise of British Empiricism: Logic and Epistemology in the British Isles (1570–1689) .
Lachman R, Lachman J L, Butterfield EC. Cognitive Psychology and Information Processing: An Introduction .
Neisser U. Cognitive Psychology: Classic edition .
Murman D. The impact of age on cognition . Semin Hear . 2015;36(3):111-121. doi:10.1055/s-0035-1555115
Li S, Weinstein G, Zare H, et al. The genetics of circulating BDNF: Towards understanding the role of BDNF in brain structure and function in middle and old ages . Brain Commun . 2020;2(2):fcaa176. doi:10.1093/braincomms/fcaa176
Weinsten Y. How long is short-term memory: Shorter than you might think . Duke Undergraduate Education.
Leding J, Antonio L. Need for cognition and discrepancy detection in the misinformation effect . J Cognitive Psychol . 2019;31(4):409-415. doi:10.1080/20445911.2019.1626400
Scheiter K, Schubert C, Schuler A. Self-regulated learning from illustrated text: Eye movement modelling to support use and regulation of cognitive processes during learning from multimedia . Brit J Educ Psychol . 2017;88(1):80-94. doi:10.1111/bjep.12175
Toppi J, Astolfi L, Risetti M, et al. Different topological properties of EEG-derived networks describe working memory phases as revealed by graph theoretical analysis . Front Hum Neurosci . 2018;11:637. doi:10.3389/fnhum.2017.00637
Mather G. Foundations of sensation and perception .
Sousa D. How the brain learns .
Houben S, Otgaar H, Roelofs J, Merckelbach H. EMDR and false memories: A response to Lee, de Jongh, and Hase (2019) . Clin Psycholog Sci . 2019;7(3):405-6. doi:10.1177/2167702619830392
Schwarzer R. Self-efficacy: Thought control of action .
Imaoka M, Nakao H, Nakamura M, et al. Effect of multicomponent exercise and nutrition support on the cognitive function of older adults: A randomized controlled trial . Clin Interv Aging . 2019;14:2145-53. doi:10.2147/CIA.S229034
Petroutsatou K, Sifiniadis A. Exploring the consequences of human multitasking in industrial automation projects: A tool to mitigate impacts - Part II . Organiz Techn Manage Construct . 2018;10(1):1770-1777. doi:10.2478/otmcj-2016-0031
Mullis CE, Hatfield RC. The effects of multitasking on auditors' judgment quality . Contemp Account Res . 2017;35(1):314-333. doi:10.1111/1911-3846.12392
Revlin R. Cognition: Theory and Practice .
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Higher Order Thinking: Bloom’s Taxonomy
Many students start college using the study strategies they used in high school, which is understandable—the strategies worked in the past, so why wouldn’t they work now? As you may have already figured out, college is different. Classes may be more rigorous (yet may seem less structured), your reading load may be heavier, and your professors may be less accessible. For these reasons and others, you’ll likely find that your old study habits aren’t as effective as they used to be. Part of the reason for this is that you may not be approaching the material in the same way as your professors. In this handout, we provide information on Bloom’s Taxonomy—a way of thinking about your schoolwork that can change the way you study and learn to better align with how your professors think (and how they grade).
Why higher order thinking leads to effective study
Most students report that high school was largely about remembering and understanding large amounts of content and then demonstrating this comprehension periodically on tests and exams. Bloom’s Taxonomy is a framework that starts with these two levels of thinking as important bases for pushing our brains to five other higher order levels of thinking—helping us move beyond remembering and recalling information and move deeper into application, analysis, synthesis, evaluation, and creation—the levels of thinking that your professors have in mind when they are designing exams and paper assignments. Because it is in these higher levels of thinking that our brains truly and deeply learn information, it’s important that you integrate higher order thinking into your study habits.
The following categories can help you assess your comprehension of readings, lecture notes, and other course materials. By creating and answering questions from a variety of categories, you can better anticipate and prepare for all types of exam questions. As you learn and study, start by asking yourself questions and using study methods from the level of remembering. Then, move progressively through the levels to push your understanding deeper—making your studying more meaningful and improving your long-term retention.
Level 1: Remember
This level helps us recall foundational or factual information: names, dates, formulas, definitions, components, or methods.
Make and use flashcards for key terms. | How would you define…? |
Make a list or timeline of the main events. | List the _________ in order. |
List the main characteristics of something. | Who were…? |
Level 2: Understand
Understanding means that we can explain main ideas and concepts and make meaning by interpreting, classifying, summarizing, inferring, comparing, and explaining.
Discuss content with or explain to a partner. | How would you differentiate between _____ and _____? |
Explain the main idea of the section. | What is the main idea of ________? |
Write a summary of the chapter in your own words. | Why did…? |
Level 3: Apply
Application allows us to recognize or use concepts in real-world situations and to address when, where, or how to employ methods and ideas.
Seek concrete examples of abstract ideas. | Why does _________ work? |
Work practice problems and exercises. | How would you change________? |
Write an instructional manual or study guide on the chapter that others could use. | How would you develop a set of instructions about…? |
Level 4: Analyze
Analysis means breaking a topic or idea into components or examining a subject from different perspectives. It helps us see how the “whole” is created from the “parts.” It’s easy to miss the big picture by getting stuck at a lower level of thinking and simply remembering individual facts without seeing how they are connected. Analysis helps reveal the connections between facts.
Generate a list of contributing factors. | How does this element contribute to the whole? |
Determine the importance of different elements or sections | What is the significance of this section? |
Think about it from a different perspective | How would _______ group see this? |
Level 5: Synthesize
Synthesizing means considering individual elements together for the purpose of drawing conclusions, identifying themes, or determining common elements. Here you want to shift from “parts” to “whole.”
Generalize information from letures and readings. | Develop a proposal that would… |
Condense and re-state the content in one or two sentences. | How can you paraphrase this information into 1-2 concise sentences? |
Compare and contrast. | What makes ________ similar and different from __________? |
Level 6: Evaluate
Evaluating means making judgments about something based on criteria and standards. This requires checking and critiquing an argument or concept to form an opinion about its value. Often there is not a clear or correct answer to this type of question. Rather, it’s about making a judgment and supporting it with reasons and evidence.
Decide if you like, dislike, agree, or disagree with an author or a decision. | What is your opinion about ________? What evidence and reasons support your opinion? |
Consider what you would do if asked to make a choice. | How would you improve this? |
Determine which approach or argument is most effective. | Which argument or approach is stronger? Why? |
Level 7: Create
Creating involves putting elements together to form a coherent or functional whole. Creating includes reorganizing elements into a new pattern or structure through planning. This is the highest and most advanced level of Bloom’s Taxonomy.
Build a model and use it to teach the information to others. | How can you create a model and use it to teach this information to others? |
Design an experiment. | What experiment can you make to demonstrate or test this information? |
Write a short story about the concept. | How can this information be told in the form of a story or poem? |
Pairing Bloom’s Taxonomy with other effective study strategies
While higher order thinking is an excellent way to approach learning new information and studying, you should pair it with other effective study strategies. Check out some of these links to read up on other tools and strategies you can try:
- Study Smarter, Not Harder
- Simple Study Template
- Using Concept Maps
- Group Study
- Evidence-Based Study Strategies Video
- Memory Tips Video
- All of our resources
Other UNC resources
If you’d like some individual assistance using higher order questions (or with anything regarding your academic success), check out some of your UNC resources:
- Academic Coaching: Make an appointment with an academic coach at the Learning Center to discuss your study habits one-on-one.
- Office Hours : Make an appointment with your professor or TA to discuss course material and how to be successful in the class.
Works consulted
Anderson, L. W., Krathwohl, D.R., Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., Wittrock, M.C (2001). A taxonomy of learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York, NY: Longman.
“Bloom’s Taxonomy.” University of Waterloo. Retrieved from https://uwaterloo.ca/centre-for-teaching-excellence/teaching-resources/teaching-tips/planning-courses-and-assignments/course-design/blooms-taxonomy
“Bloom’s Taxonomy.” Retrieved from http://www.bloomstaxonomy.org/Blooms%20Taxonomy%20questions.pdf
Overbaugh, R., and Schultz, L. (n.d.). “Image of two versions of Bloom’s Taxonomy.” Norfolk, VA: Old Dominion University. Retrieved from https://www.odu.edu/content/dam/odu/col-dept/teaching-learning/docs/blooms-taxonomy-handout.pdf
If you enjoy using our handouts, we appreciate contributions of acknowledgement.
Make a Gift
- Campus Life
- ...a student.
- ...a veteran.
- ...an alum.
- ...a parent.
- ...faculty or staff.
- UTC Learn (Canvas)
- Class Schedule
- Crisis Resources
- People Finder
- Change Password
UTC RAVE Alert
Critical thinking and problem-solving, jump to: , what is critical thinking, characteristics of critical thinking, why teach critical thinking.
- Teaching Strategies to Help Promote Critical Thinking Skills
References and Resources
When examining the vast literature on critical thinking, various definitions of critical thinking emerge. Here are some samples:
- "Critical thinking is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action" (Scriven, 1996).
- "Most formal definitions characterize critical thinking as the intentional application of rational, higher order thinking skills, such as analysis, synthesis, problem recognition and problem solving, inference, and evaluation" (Angelo, 1995, p. 6).
- "Critical thinking is thinking that assesses itself" (Center for Critical Thinking, 1996b).
- "Critical thinking is the ability to think about one's thinking in such a way as 1. To recognize its strengths and weaknesses and, as a result, 2. To recast the thinking in improved form" (Center for Critical Thinking, 1996c).
Perhaps the simplest definition is offered by Beyer (1995) : "Critical thinking... means making reasoned judgments" (p. 8). Basically, Beyer sees critical thinking as using criteria to judge the quality of something, from cooking to a conclusion of a research paper. In essence, critical thinking is a disciplined manner of thought that a person uses to assess the validity of something (statements, news stories, arguments, research, etc.).
Back
Wade (1995) identifies eight characteristics of critical thinking. Critical thinking involves asking questions, defining a problem, examining evidence, analyzing assumptions and biases, avoiding emotional reasoning, avoiding oversimplification, considering other interpretations, and tolerating ambiguity. Dealing with ambiguity is also seen by Strohm & Baukus (1995) as an essential part of critical thinking, "Ambiguity and doubt serve a critical-thinking function and are a necessary and even a productive part of the process" (p. 56).
Another characteristic of critical thinking identified by many sources is metacognition. Metacognition is thinking about one's own thinking. More specifically, "metacognition is being aware of one's thinking as one performs specific tasks and then using this awareness to control what one is doing" (Jones & Ratcliff, 1993, p. 10 ).
In the book, Critical Thinking, Beyer elaborately explains what he sees as essential aspects of critical thinking. These are:
- Dispositions: Critical thinkers are skeptical, open-minded, value fair-mindedness, respect evidence and reasoning, respect clarity and precision, look at different points of view, and will change positions when reason leads them to do so.
- Criteria: To think critically, must apply criteria. Need to have conditions that must be met for something to be judged as believable. Although the argument can be made that each subject area has different criteria, some standards apply to all subjects. "... an assertion must... be based on relevant, accurate facts; based on credible sources; precise; unbiased; free from logical fallacies; logically consistent; and strongly reasoned" (p. 12).
- Argument: Is a statement or proposition with supporting evidence. Critical thinking involves identifying, evaluating, and constructing arguments.
- Reasoning: The ability to infer a conclusion from one or multiple premises. To do so requires examining logical relationships among statements or data.
- Point of View: The way one views the world, which shapes one's construction of meaning. In a search for understanding, critical thinkers view phenomena from many different points of view.
- Procedures for Applying Criteria: Other types of thinking use a general procedure. Critical thinking makes use of many procedures. These procedures include asking questions, making judgments, and identifying assumptions.
Oliver & Utermohlen (1995) see students as too often being passive receptors of information. Through technology, the amount of information available today is massive. This information explosion is likely to continue in the future. Students need a guide to weed through the information and not just passively accept it. Students need to "develop and effectively apply critical thinking skills to their academic studies, to the complex problems that they will face, and to the critical choices they will be forced to make as a result of the information explosion and other rapid technological changes" (Oliver & Utermohlen, p. 1 ).
As mentioned in the section, Characteristics of Critical Thinking , critical thinking involves questioning. It is important to teach students how to ask good questions, to think critically, in order to continue the advancement of the very fields we are teaching. "Every field stays alive only to the extent that fresh questions are generated and taken seriously" (Center for Critical Thinking, 1996a ).
Beyer sees the teaching of critical thinking as important to the very state of our nation. He argues that to live successfully in a democracy, people must be able to think critically in order to make sound decisions about personal and civic affairs. If students learn to think critically, then they can use good thinking as the guide by which they live their lives.
Teaching Strategies to Help Promote Critical Thinking
The 1995, Volume 22, issue 1, of the journal, Teaching of Psychology , is devoted to the teaching critical thinking. Most of the strategies included in this section come from the various articles that compose this issue.
- CATS (Classroom Assessment Techniques): Angelo stresses the use of ongoing classroom assessment as a way to monitor and facilitate students' critical thinking. An example of a CAT is to ask students to write a "Minute Paper" responding to questions such as "What was the most important thing you learned in today's class? What question related to this session remains uppermost in your mind?" The teacher selects some of the papers and prepares responses for the next class meeting.
- Cooperative Learning Strategies: Cooper (1995) argues that putting students in group learning situations is the best way to foster critical thinking. "In properly structured cooperative learning environments, students perform more of the active, critical thinking with continuous support and feedback from other students and the teacher" (p. 8).
- Case Study /Discussion Method: McDade (1995) describes this method as the teacher presenting a case (or story) to the class without a conclusion. Using prepared questions, the teacher then leads students through a discussion, allowing students to construct a conclusion for the case.
- Using Questions: King (1995) identifies ways of using questions in the classroom:
- Reciprocal Peer Questioning: Following lecture, the teacher displays a list of question stems (such as, "What are the strengths and weaknesses of...). Students must write questions about the lecture material. In small groups, the students ask each other the questions. Then, the whole class discusses some of the questions from each small group.
- Reader's Questions: Require students to write questions on assigned reading and turn them in at the beginning of class. Select a few of the questions as the impetus for class discussion.
- Conference Style Learning: The teacher does not "teach" the class in the sense of lecturing. The teacher is a facilitator of a conference. Students must thoroughly read all required material before class. Assigned readings should be in the zone of proximal development. That is, readings should be able to be understood by students, but also challenging. The class consists of the students asking questions of each other and discussing these questions. The teacher does not remain passive, but rather, helps "direct and mold discussions by posing strategic questions and helping students build on each others' ideas" (Underwood & Wald, 1995, p. 18 ).
- Use Writing Assignments: Wade sees the use of writing as fundamental to developing critical thinking skills. "With written assignments, an instructor can encourage the development of dialectic reasoning by requiring students to argue both [or more] sides of an issue" (p. 24).
- Written dialogues: Give students written dialogues to analyze. In small groups, students must identify the different viewpoints of each participant in the dialogue. Must look for biases, presence or exclusion of important evidence, alternative interpretations, misstatement of facts, and errors in reasoning. Each group must decide which view is the most reasonable. After coming to a conclusion, each group acts out their dialogue and explains their analysis of it.
- Spontaneous Group Dialogue: One group of students are assigned roles to play in a discussion (such as leader, information giver, opinion seeker, and disagreer). Four observer groups are formed with the functions of determining what roles are being played by whom, identifying biases and errors in thinking, evaluating reasoning skills, and examining ethical implications of the content.
- Ambiguity: Strohm & Baukus advocate producing much ambiguity in the classroom. Don't give students clear cut material. Give them conflicting information that they must think their way through.
- Angelo, T. A. (1995). Beginning the dialogue: Thoughts on promoting critical thinking: Classroom assessment for critical thinking. Teaching of Psychology, 22(1), 6-7.
- Beyer, B. K. (1995). Critical thinking. Bloomington, IN: Phi Delta Kappa Educational Foundation.
- Center for Critical Thinking (1996a). The role of questions in thinking, teaching, and learning. [On-line]. Available HTTP: http://www.criticalthinking.org/University/univlibrary/library.nclk
- Center for Critical Thinking (1996b). Structures for student self-assessment. [On-line]. Available HTTP: http://www.criticalthinking.org/University/univclass/trc.nclk
- Center for Critical Thinking (1996c). Three definitions of critical thinking [On-line]. Available HTTP: http://www.criticalthinking.org/University/univlibrary/library.nclk
- Cooper, J. L. (1995). Cooperative learning and critical thinking. Teaching of Psychology, 22(1), 7-8.
- Jones, E. A. & Ratcliff, G. (1993). Critical thinking skills for college students. National Center on Postsecondary Teaching, Learning, and Assessment, University Park, PA. (Eric Document Reproduction Services No. ED 358 772)
- King, A. (1995). Designing the instructional process to enhance critical thinking across the curriculum: Inquiring minds really do want to know: Using questioning to teach critical thinking. Teaching of Psychology, 22 (1) , 13-17.
- McDade, S. A. (1995). Case study pedagogy to advance critical thinking. Teaching Psychology, 22(1), 9-10.
- Oliver, H. & Utermohlen, R. (1995). An innovative teaching strategy: Using critical thinking to give students a guide to the future.(Eric Document Reproduction Services No. 389 702)
- Robertson, J. F. & Rane-Szostak, D. (1996). Using dialogues to develop critical thinking skills: A practical approach. Journal of Adolescent & Adult Literacy, 39(7), 552-556.
- Scriven, M. & Paul, R. (1996). Defining critical thinking: A draft statement for the National Council for Excellence in Critical Thinking. [On-line]. Available HTTP: http://www.criticalthinking.org/University/univlibrary/library.nclk
- Strohm, S. M., & Baukus, R. A. (1995). Strategies for fostering critical thinking skills. Journalism and Mass Communication Educator, 50 (1), 55-62.
- Underwood, M. K., & Wald, R. L. (1995). Conference-style learning: A method for fostering critical thinking with heart. Teaching Psychology, 22(1), 17-21.
- Wade, C. (1995). Using writing to develop and assess critical thinking. Teaching of Psychology, 22(1), 24-28.
Other Reading
- Bean, J. C. (1996). Engaging ideas: The professor's guide to integrating writing, critical thinking, & active learning in the classroom. Jossey-Bass.
- Bernstein, D. A. (1995). A negotiation model for teaching critical thinking. Teaching of Psychology, 22(1), 22-24.
- Carlson, E. R. (1995). Evaluating the credibility of sources. A missing link in the teaching of critical thinking. Teaching of Psychology, 22(1), 39-41.
- Facione, P. A., Sanchez, C. A., Facione, N. C., & Gainen, J. (1995). The disposition toward critical thinking. The Journal of General Education, 44(1), 1-25.
- Halpern, D. F., & Nummedal, S. G. (1995). Closing thoughts about helping students improve how they think. Teaching of Psychology, 22(1), 82-83.
- Isbell, D. (1995). Teaching writing and research as inseparable: A faculty-librarian teaching team. Reference Services Review, 23(4), 51-62.
- Jones, J. M. & Safrit, R. D. (1994). Developing critical thinking skills in adult learners through innovative distance learning. Paper presented at the International Conference on the practice of adult education and social development. Jinan, China. (Eric Document Reproduction Services No. ED 373 159)
- Sanchez, M. A. (1995). Using critical-thinking principles as a guide to college-level instruction. Teaching of Psychology, 22(1), 72-74.
- Spicer, K. L. & Hanks, W. E. (1995). Multiple measures of critical thinking skills and predisposition in assessment of critical thinking. Paper presented at the annual meeting of the Speech Communication Association, San Antonio, TX. (Eric Document Reproduction Services No. ED 391 185)
- Terenzini, P. T., Springer, L., Pascarella, E. T., & Nora, A. (1995). Influences affecting the development of students' critical thinking skills. Research in Higher Education, 36(1), 23-39.
On the Internet
- Carr, K. S. (1990). How can we teach critical thinking. Eric Digest. [On-line]. Available HTTP: http://ericps.ed.uiuc.edu/eece/pubs/digests/1990/carr90.html
- The Center for Critical Thinking (1996). Home Page. Available HTTP: http://www.criticalthinking.org/University/
- Ennis, Bob (No date). Critical thinking. [On-line], April 4, 1997. Available HTTP: http://www.cof.orst.edu/cof/teach/for442/ct.htm
- Montclair State University (1995). Curriculum resource center. Critical thinking resources: An annotated bibliography. [On-line]. Available HTTP: http://www.montclair.edu/Pages/CRC/Bibliographies/CriticalThinking.html
- No author, No date. Critical Thinking is ... [On-line], April 4, 1997. Available HTTP: http://library.usask.ca/ustudy/critical/
- Sheridan, Marcia (No date). Internet education topics hotlink page. [On-line], April 4, 1997. Available HTTP: http://sun1.iusb.edu/~msherida/topics/critical.html
Walker Center for Teaching and Learning
- 433 Library
- Dept 4354
- 615 McCallie Ave
- 423-425-4188
An official website of the United States government
The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.
The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
- Publications
- Account settings
Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .
- Advanced Search
- Journal List
Analysing Complex Problem-Solving Strategies from a Cognitive Perspective: The Role of Thinking Skills
1 MTA-SZTE Digital Learning Technologies Research Group, Center for Learning and Instruction, University of Szeged, 6722 Szeged, Hungary
Gyöngyvér Molnár
2 MTA-SZTE Digital Learning Technologies Research Group, Institute of Education, University of Szeged, 6722 Szeged, Hungary; uh.degezs-u.yspde@ranlomyg
Associated Data
The data used to support the findings cannot be shared at this time as it also forms part of an ongoing study.
Complex problem solving (CPS) is considered to be one of the most important skills for successful learning. In an effort to explore the nature of CPS, this study aims to investigate the role of inductive reasoning (IR) and combinatorial reasoning (CR) in the problem-solving process of students using statistically distinguishable exploration strategies in the CPS environment. The sample was drawn from a group of university students (N = 1343). The tests were delivered via the eDia online assessment platform. Latent class analyses were employed to seek students whose problem-solving strategies showed similar patterns. Four qualitatively different class profiles were identified: (1) 84.3% of the students were proficient strategy users, (2) 6.2% were rapid learners, (3) 3.1% were non-persistent explorers, and (4) 6.5% were non-performing explorers. Better exploration strategy users showed greater development in thinking skills, and the roles of IR and CR in the CPS process were varied for each type of strategy user. To sum up, the analysis identified students’ problem-solving behaviours in respect of exploration strategy in the CPS environment and detected a number of remarkable differences in terms of the use of thinking skills between students with different exploration strategies.
1. Introduction
Problem solving is part and parcel of our daily activities, for instance, in determining what to wear in the morning, how to use our new electronic devices, how to reach a restaurant by public transport, how to arrange our schedule to achieve the greatest work efficiency and how to communicate with people in a foreign country. In most cases, it is essential to solve the problems that recur in our study, work and daily lives. These situations require problem solving. Generally, problem solving is the thinking that occurs if we want “to overcome barriers between a given state and a desired goal state by means of behavioural and/or cognitive, multistep activities” ( Frensch and Funke 1995, p. 18 ). It has also been considered as one of the most important skills for successful learning in the 21st century. This study focuses on one specific kind of problem solving, complex problem solving (CPS). (Numerous other terms are also used ( Funke et al. 2018 ), such as interactive problem solving ( Greiff et al. 2013 ; Wu and Molnár 2018 ), and creative problem solving ( OECD 2010 ), etc.).
CPS is a transversal skill ( Greiff et al. 2014 ), operating several mental activities and thinking skills (see Molnár et al. 2013 ). In order to explore the nature of CPS, some studies have focused on detecting its component skills ( Wu and Molnár 2018 ), whereas others have analysed students’ behaviour during the problem-solving process ( Greiff et al. 2018 ; Wu and Molnár 2021 ). This study aims to link these two fields by investigating the role of thinking skills in learning by examining students’ use of statistically distinguishable exploration strategies in the CPS environment.
1.1. Complex Problem Solving: Definition, Assessment and Relations to Intelligence
According to a widely accepted definition proposed by Buchner ( 1995 ), CPS is “the successful interaction with task environments that are dynamic (i.e., change as a function of users’ intervention and/or as a function of time) and in which some, if not all, of the environment’s regularities can only be revealed by successful exploration and integration of the information gained in that process” ( Buchner 1995, p. 14 ). A CPS process is split into two phases, knowledge acquisition and knowledge application. In the knowledge acquisition (KAC) phase of CPS, the problem solver understands the problem itself and stores the acquired information ( Funke 2001 ; Novick and Bassok 2005 ). In the knowledge application (KAP) phase, the problem solver applies the acquired knowledge to bring about the transition from a given state to a goal state ( Novick and Bassok 2005 ).
Problem solving, especially CPS, has frequently been compared or linked to intelligence in previous studies (e.g., Beckmann and Guthke 1995 ; Stadler et al. 2015 ; Wenke et al. 2005 ). Lotz et al. ( 2017 ) observed that “intelligence and [CPS] are two strongly overlapping constructs” (p. 98). There are many similarities and commonalities that can be detected between CPS and intelligence. For instance, CPS and intelligence share some of the same key features, such as the integration of information ( Stadler et al. 2015 ). Furthermore, Wenke et al. ( 2005 ) stated that “the ability to solve problems has featured prominently in virtually every definition of human intelligence” (p. 9); meanwhile, from the opposite perspective, intelligence has also been considered as one of the most important predictors of the ability to solve problems ( Wenke et al. 2005 ). Moreover, the relation between CPS and intelligence has also been discussed from an empirical perspective. A meta-analysis conducted by Stadler et al. ( 2015 ) selected 47 empirical studies (total sample size N = 13,740) which focused on the correlation between CPS and intelligence. The results of their analysis confirmed that a correlation between CPS and intelligence exists with a moderate effect size of M(g) = 0.43.
Due to the strong link between CPS and intelligence, assessments of these two domains have been connected and have overlapped to a certain extent. For instance, Beckmann and Guthke ( 1995 ) observed that some of the intelligence tests “capture something akin to an individual’s general ability to solve problems (e.g., Sternberg 1982 )” (p. 184). Nowadays, some widely used CPS assessment methods are related to intelligence but still constitute a distinct construct ( Schweizer et al. 2013 ), such as the MicroDYN approach ( Greiff and Funke 2009 ; Greiff et al. 2012 ; Schweizer et al. 2013 ). This approach uses the minimal complex system to simulate simplistic, artificial but still complex problems following certain construction rules ( Greiff and Funke 2009 ; Greiff et al. 2012 ).
The MicroDYN approach has been widely employed to measure problem solving in a well-defined problem context (i.e., “problems have a clear set of means for reaching a precisely described goal state”, Dörner and Funke 2017, p. 1 ). To complete a task based on the MicroDYN approach, the problem solver engages in dynamic interaction with the task to acquire relevant knowledge. It is not possible to create this kind of test environment with the traditional paper-and-pencil-based method. Therefore, it is currently only possible to conduct a MicroDYN-based CPS assessment within the computer-based assessment framework. In the context of computer-based assessment, the problem-solvers’ operations were recorded and logged by the assessment platform. Thus, except for regular achievement-focused result data, logfile data are also available for analysis. This provides the option of exploring and monitoring problem solvers’ behaviour and thinking processes, specifically, their exploration strategies, during the problem-solving process (see, e.g., Chen et al. 2019 ; Greiff et al. 2015a ; Molnár and Csapó 2018 ; Molnár et al. 2022 ; Wu and Molnár 2021 ).
Problem solving, in the context of an ill-defined problem (i.e., “problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear”, Dörner and Funke 2017, p. 1), involved a different cognitive process than that in the context of a well-defined problem ( Funke 2010 ; Schraw et al. 1995 ), and it cannot be measured with the MicroDYN approach. The nature of ill-defined problem solving has been explored and discussed in numerous studies (e.g., Dörner and Funke 2017 ; Hołda et al. 2020 ; Schraw et al. 1995 ; Welter et al. 2017 ). This will not be discussed here as this study focuses on well-defined problem solving.
1.2. Inductive and Combinatorial Reasoning as Component Skills of Complex Problem Solving
Frensch and Funke ( 1995 ) constructed a theoretical framework that summarizes the basic components of CPS and the interrelations among the components. The framework contains three separate components: problem solver, task and environment. The impact of the problem solver is mainly relevant to three main categories, which are memory contents, dynamic information processing and non-cognitive variables. Some thinking skills have been reported to play an important role in dynamic information processing. We can thus describe them as component skills of CPS. Inductive reasoning (IR) and combinatorial reasoning (CR) are the two thinking skills that have been most frequently discussed as component skills of CPS.
IR is the reasoning skill that has been covered most commonly in the literature. Currently, there is no universally accepted definition. Molnár et al. ( 2013 ) described it as the cognitive process of acquiring general regularities by generalizing single and specific observations and experiences, whereas Klauer ( 1990 ) defined it as the discovery of regularities that relies upon the detection of similarities and/or dissimilarities as concerns attributes of or relations to or between objects. Sandberg and McCullough ( 2010 ) provided a general conclusion of the definitions of IR: it is the process of moving from the specific to the general.
Csapó ( 1997 ) pointed out that IR is a basic component of thinking and that it forms a central aspect of intellectual functioning. Some studies have also discussed the role of IR in a problem-solving environment. For instance, Mayer ( 1998 ) stated that IR will be applied in information processing during the process of solving general problems. Gilhooly ( 1982 ) also pointed out that IR plays a key role in some activities in the problem-solving process, such as hypothesis generation and hypothesis testing. Moreover, the influence of IR on both KAC and KAP has been analysed and demonstrated in previous studies ( Molnár et al. 2013 ).
Empirical studies have also provided evidence that IR and CPS are related. Based on the results of a large-scale assessment (N = 2769), Molnár et al. ( 2013 ) showed that IR significantly correlated with 9–17-year-old students’ domain-general problem-solving achievement (r = 0.44–0.52). Greiff et al. ( 2015b ) conducted a large-scale assessment project (N = 2021) in Finland to explore the links between fluid reasoning skills and domain-general CPS. The study measured fluid reasoning as a two-dimensional model which consisted of deductive reasoning and scientific reasoning and included inductive thinking processes ( Greiff et al. 2015b ). The results drawing on structural equation modelling indicated that fluid reasoning which was partly based on IR had significant and strong predictive effects on both KAC (β = 0.51) and KAP (β = 0.55), the two phases of problem solving. Such studies have suggested that IR is one of the component skills of CPS.
According to Adey and Csapó ’s ( 2012 ) definition, CR is the process of creating complex constructions out of a set of given elements that satisfy the conditions explicitly given in or inferred from the situation. In this process, some cognitive operations, such as combinations, arrangements, permutations, notations and formulae, will be employed ( English 2005 ). CR is one of the basic components of formal thinking ( Batanero et al. 1997 ). The relationship between CR and CPS has frequently been discussed. English ( 2005 ) demonstrated that CR has an essential meaning in several types of problem situations, such as problems requiring the systematic testing of alternative solutions. Moreover, Newell ( 1993 ) pointed out that CR is applied in some key activities of problem-solving information processing, such as strategy generation and application. Its functions include, but are not limited to, helping problem solvers to discover relationships between certain elements and concepts, promoting their fluency of thinking when they are considering different strategies ( Csapó 1999 ) and identifying all possible alternatives ( OECD 2014 ). Moreover, Wu and Molnár ’s ( 2018 ) empirical study drew on a sample (N = 187) of 11–13-year-old primary school students in China. Their study built a structural equation model between CPS, IR and CR, and the result indicated that CR showed a strong and statistically significant predictive power for CPS (β = 0.55). Thus, the results of the empirical study also support the argument that CR is one of the component skills of CPS.
1.3. Behaviours and Strategies in a Complex Problem-Solving Environment
Wüstenberg et al. ( 2012 ) stated that the creation and implementation of strategic exploration are core actions of the problem-solving task. Exploring and generating effective information are key to successfully solving a problem. Wittmann and Hattrup ( 2004 ) illustrated that “riskier strategies [create] a learning environment with greater opportunities to discover and master the rules and boundaries [of a problem]” (p. 406). Thus, when gathering information about a complex problem, there may be differences between exploration strategies in terms of efficacy. The MicroDYN scenarios, a simplification and simulation of the real-world problem-solving context, will also be influenced by the adoption and implementation of exploration strategies.
The effectiveness of the isolated variation strategy (or “Vary-One-Thing-At-A-Time” strategy—VOTAT; Vollmeyer et al. 1996 ) in a CPS environment has been hotly debated ( Chen et al. 2019 ; Greiff et al. 2018 ; Molnár and Csapó 2018 ; Molnár et al. 2022 ; Wu and Molnár 2021 ; Wüstenberg et al. 2014 ). To use the VOTAT strategy, a problem solver “systematically varies only one input variable, whereas the others remain unchanged. This way, the effect of the variable that has just been changed can be observed directly by monitoring the changes in the output variables” ( Molnár and Csapó 2018, p. 2 ). Understanding and using VOTAT effectively is the foundation for developing more complex strategies for coordinating multiple variables and the basis for some phases of scientific thinking (i.e., inquiry, analysis, inference and argument; Kuhn 2010 ; Kuhn et al. 1995 ).
Some previous studies have indicated that students who are able to apply VOTAT are more likely to achieve higher performance in a CPS assessment ( Greiff et al. 2018 ), especially if the problem is a well-defined minimal complex system (such as MicroDYN) ( Fischer et al. 2012 ; Molnár and Csapó 2018 ; Wu and Molnár 2021 ). For instance, Molnár and Csapó ( 2018 ) conducted an empirical study to explore how students’ exploration strategies influence their performance in an interactive problem-solving environment. They measured a group (N = 4371) of 3rd- to 12th-grade (aged 9–18) Hungarian students’ problem-solving achievement and modelled students’ exploration strategies. This result confirmed that students’ exploration strategies influence their problem-solving performance. For example, conscious VOTAT strategy users proved to be the best problem-solvers. Furthermore, other empirical studies (e.g., Molnár et al. 2022 ; Wu and Molnár 2021 ) achieved similar results, thus confirming the importance of VOTAT in a MicroDYN-based CPS environment.
Lotz et al. ( 2017 ) illustrated that effective use of VOTAT is associated with higher levels of intelligence. Their study also pointed out that intelligence has the potential to facilitate successful exploration behaviour. Reasoning skills are an important component of general intelligence. Based on Lotz et al. ’s ( 2017 ) statements, the roles IR and CR play in the CPS process might vary due to students’ different strategy usage patterns. However, there is still a lack of empirical studies in this regard.
2. Research Aims and Questions
Numerous studies have explored the nature of CPS, some of them discussing and analysing it from behavioural or cognitive perspectives. However, there have barely been any that have merged these two perspectives. From the cognitive perspective, this study explores the role of thinking skills (including IR and CR) in the cognition process of CPS. From the behavioural perspective, the study focuses on students’ behaviour (i.e., their exploration strategy) in the CPS assessment process. More specifically, the research aims to fill this gap and examine students’ use of statistically distinguishable exploration strategies in CPS environments and to detect the connection between the level of students’ thinking skills and their behaviour strategies in the CPS environment. The following research questions were thus formed.
- (RQ1) What exploration strategy profiles characterise the various problem-solvers at the university level?
- (RQ2) Can developmental differences in CPS, IR and CR be detected among students with different exploration strategy profiles?
- (RQ3) What are the similarities and differences in the roles IR and CR play in the CPS process as well as in the two phases of CPS (i.e., KAC and KAP) among students with different exploration strategy profiles?
3.1. Participants and Procedure
The sample was drawn from one of the largest universities in Hungary. Participation was voluntary, but students were able to earn one course credit for taking part in the assessment. The participants were students who had just started their studies there (N = 1671). 43.4% of the first-year students took part in the assessment. 50.9% of the participants were female, and 49.1% were male. We filtered the sample and excluded those who had more than 80% missing data on any of the tests. After the data were cleaned, data from 1343 students were available for analysis. The test was designed and delivered via the eDia online assessment system ( Csapó and Molnár 2019 ). The assessment was held in the university ICT room and divided into two sessions. The first session involved the CPS test, whereas the second session entailed the IR and CR tests. Each session lasted 45 min. The language of the tests was Hungarian, the mother tongue of the students.
3.2. Instruments
3.2.1. complex problem solving (cps).
The CPS assessment instrument adopted the MicroDYN approach. It contains a total of twelve scenarios, and each scenario consisted of two items (one item in the KAC phase and one item in the KAP phase in each problem scenario). Twelve KAC items and twelve KAP items were therefore delivered on the CPS test for a total of twenty-four items. Each scenario has a fictional cover story. For instance, students found a sick cat in front of their house, and they were expected to feed the cat with two different kinds of cat food to help it recover.
Each item contains up to three input and three output variables. The relations between the input and output variables were formulated with linear structural equations ( Funke 2001 ). Figure 1 shows a MicroDYN sample structure containing three input variables (A, B and C), three output variables (X, Y and Z) and a number of possible relations between the variables. The complexity of the item was defined by the number of input and output variables, and the number of relations between the variables. The test began with the item with the lowest complexity. The complexity of each item gradually increased as the test progressed.
A typical MicroDYN structure with three input variables and three output variables ( Greiff and Funke 2009 ).
The interface of each item displays the value of each variable in both numerical and figural forms (See Figure 2 ). Each of the input variables has a controller, which makes it possible to vary and set the value between +2 (+ +) and −2 (− −). To operate the system, students need to click the “+” or “−” button or use the slider directly to select the value they want to be added to or subtracted from the current value of the input variable. After clicking the “Apply” button in the interface, the input variables will add or subtract the selected value, and the output variables will show the corresponding changes. The history of the values for the input and output variables within the same problem scenario is displayed on screen. If students want to withdraw all the changes and set all the variables to their original status, they can click the “Reset” button.
Screenshot of the MicroDYN item Cat—first phase (knowledge acquisition). (The items were administered in Hungarian.)
In the first phase of the problem-solving process, the KAC phase, students are asked to interact with the system by changing the value of the input variables and observing and analysing the corresponding changes in the output variables. They are then expected to determine the relationship between the input and output variables and draw it in the form of (an) arrow(s) on the concept map at the bottom of the interface. To avoid item dependence in the second phase of the problem-solving process, the students are provided with a concept map during the KAP phase (see Figure 3 ), which shows the correct connections between the input and output variables. The students are expected to interact with the system by manipulating the input variables to make the output variables reach the given target values in four steps or less. That is, they cannot click on the “Apply” button more than four times. The first phase had a 180 s time limit, whereas the second had a 90 s time limit.
Screenshot of the MicroDYN item Cat—second phase (knowledge application). (The items were administered in Hungarian).
3.2.2. Inductive Reasoning (IR)
The IR instrument (see Figure 4 ) was originally designed and developed in Hungary ( Csapó 1997 ). In the last 25 years, the instrument has been further developed and scaled for a wide age range ( Molnár and Csapó 2011 ). In addition, figural items have been added, and the assessment method has evolved from paper-and-pencil to computer-based ( Pásztor 2016 ). Currently, the instrument is widely employed in a number of countries (see, e.g., Mousa and Molnár 2020 ; Pásztor et al. 2018 ; Wu et al. 2022 ; Wu and Molnár 2018 ). In the present study, four types of items were included after test adaptation: figural series, figural analogies, number analogies and number series. Students were expected to ascertain the correct relationship between the given figures and numbers and select a suitable figure or number as their answer. Students used the drag-and-drop operation to provide their answers. In total, 49 inductive reasoning items were delivered to the participating students.
Sample items for the IR test. (The items were administered in Hungarian.).
3.2.3. Combinatorial Reasoning (CR)
The CR instrument (see Figure 5 ) was originally designed by Csapó ( 1988 ). The instrument was first developed in paper-and-pencil format and then modified for computer use ( Pásztor and Csapó 2014 ). Each item contained figural or verbal elements and a clear requirement for combing through the elements. Students were asked to list every single combination based on a given rule they could find. For the figural items, students provided their answers using the drag-and-drop operation; for the verbal items, they were asked to type their answers in a text box provided on screen. The test consisted of eight combinatorial reasoning items in total.
Sample item for the CR test. (The items were administered in Hungarian).
3.3. Scoring
Students’ performance was automatically scored via the eDia platform. Items on the CPS and IR tests were scored dichotomously. In the first phase (KAC) of the CPS test, if a student drew all the correct relations on the concept map provided on screen within the given timeframe, his/her performance was assigned a score of 1 or otherwise a score of 0. In the second phase (KAP) of the CPS test, if the student successfully reached the given target values of the output variables by manipulating the level of the input variables within no more than four steps and the given timeframe, then his/her performance earned a score of 1 or otherwise a score of 0. On the IR test items, if a student selected the correct figure or number as his/her answer, then he or she received a score of 1; otherwise, the score was 0.
Students’ performance on the CR test items was scored according to a special J index, which was developed by Csapó ( 1988 ). The J index ranges from 0 to 1, where 1 means that the student provided all the correct combinations without any redundant combinations on the task. The formula for computing the J index is the following:
x stands for the number of correct combinations in the student’s answer,
T stands for the number of all possible correct combinations, and
y stands for the number of redundant combinations in the student’s answer.
Furthermore, according to Csapó ’s ( 1988 ) design, if y is higher than T, then the J index will be counted as 0.
3.4. Coding and Labelling the Logfile Data
Beyond concrete answer data, students’ interaction and manipulation behaviour were also logged in the assessment system. This made it possible to analyse students’ exploration behaviour in the first phase of the CPS process (KAC phase). Toward this aim, we adopted a labelling system developed by Molnár and Csapó ( 2018 ) to transfer the raw logfile data to structured data files for analysis. Based on the system, each trial (i.e., the sum of manipulations within the same problem scenario which was applied and tested by clicking the “Apply” button) was modelled as a single data entity. The sum of these trials within the same problem was defined as a strategy. In our study, we only consider the trials which were able to provide useful and new information for the problem-solvers, whereas the redundant or operations trials were excluded.
In this study, we analysed students’ trials to determine the extent to which they used the VOTAT strategy: fully, partially or not at all. This strategy is the most successful exploration strategy for such problems; it is the easiest to interpret and provides direct information about the given variable without any mediation effects ( Fischer et al. 2012 ; Greiff et al. 2018 ; Molnár and Csapó 2018 ; Wüstenberg et al. 2014 ; Wu and Molnár 2021 ). Based on the definition of VOTAT noted in Section 1.3 , we checked students’ trials to ascertain if they systematically varied one input variable while keeping the others unchanged, or applied a different, less successful strategy. We considered the following three types of trials:
- “Only one single input variable was manipulated, whose relationship to the output variables was unknown (we considered a relationship unknown if its effect cannot be known from previous settings), while the other variables were set at a neutral value like zero […]
- One single input variable was changed, whose relationship to the output variables was unknown. The others were not at zero, but at a setting used earlier. […]
- One single input variable was changed, whose relationship to the output variables was unknown, and the others were not at zero; however, the effect of the other input variable(s) was known from earlier settings. Even so, this combination was not attempted earlier” ( Molnár and Csapó 2018, p. 8 )
We used the numbers 0, 1 and 2 to distinguish the level of students’ use of the most effective exploration strategy (i.e., VOTAT). If a student applied one or more of the above trials for every input variable within the same scenario, we considered that they had used the full VOTAT strategy and labelled this behaviour 2. If a student had only employed VOTAT on some but not all of the input variables, we concluded that they had used a partial VOTAT strategy for that problem scenario and labelled it 1. If a student had used none of the trials noted above in their problem exploration, then we determined that they had not used VOTAT at all and thus gave them a label of 0.
3.5. Data Analysis Plan
We used LCA (latent class analysis) to explore students’ exploration strategy profiles. LCA is a latent variable modelling approach that can be used to identify unmeasured (latent) classes of samples with similarly observed variables. LCA has been widely used in analysing logfile data for CPS assessment and in exploring students’ behaviour patterns (see, e.g., Gnaldi et al. 2020 ; Greiff et al. 2018 ; Molnár et al. 2022 ; Molnár and Csapó 2018 ; Mustafić et al. 2019 ; Wu and Molnár 2021 ). The scores for the use of VOTAT in the KAC phase (0, 1, 2; see Section 3.4 ) were used for the LCA analysis. We used Mplus ( Muthén and Muthén 2010 ) to run the LCA analysis. Several indices were used to measure the model fit: AIC (Akaike information criterion), BIC (Bayesian information criterion) and aBIC (adjusted Bayesian information criterion). With these three indicators, lower values indicate a better model fit. Entropy (ranging from 0 to 1, with values close to 1 indicating high certainty in the classification). The Lo–Mendell–Rubin adjusted likelihood ratio was used to compare the model containing n latent classes with the model containing n − 1 latent classes, and the p value was the indicator for whether a significant difference could be detected ( Lo et al. 2001 ). The results of the Lo–Mendell–Rubin adjusted likelihood ratio analysis were used to decide the correct number of latent classes in LCA models.
ANOVA was used to analyse the performance differences for CPS, IR and CR across the students from the different class profiles. The analysis was run using SPSS. A path analysis (PA) was employed in the structural equation modelling (SEM) framework to investigate the roles of CR and IR in CPS and the similarities and differences across the students from the different exploration strategy profiles. The PA models were carried out with Mplus. The Tucker–Lewis index (TLI), the comparative fit index (CFI) and the root-mean-square error of approximation (RMSEA) were used as indicators for the model fit. A TLI and CFI larger than 0.90 paired with a RMSEA less than 0.08 are commonly considered as an acceptable model fit ( van de Schoot et al. 2012 ).
4.1. Descriptive Results
All three tests showed good reliability (Cronbach’s α: CPS: 0.89; IR: 0.87; CR: 0.79). Furthermore, the two sub-dimensions of the CPS test, KAC and KAP, also showed satisfactory reliability (Cronbach’s α: KAC: 0.86; KAP: 0.78). The tests thus proved to be reliable. The means and standard deviations of students’ performance (in percentage) on each test are provided in Table 1 .
The means and standard deviations of students’ performance on each test.
CPS | IR | CR | |||
---|---|---|---|---|---|
Overall | KAC | KAP | |||
Mean (%) | 56.21 | 62.93 | 49.50 | 65.83 | 68.46 |
S.D. (%) | 22.37 | 26.65 | 22.75 | 15.41 | 20.02 |
4.2. Four Qualitatively Different Exploration Strategy Profiles Can Be Distinguished in CPS
Based on the labelled logfile data for CPS, we applied latent class analyses to identify the behaviour patterns of the students in the exploration phase of the problem-solving process. The model fits for the LCA analysis are listed in Table 2 . Compared with the 2 or 3 latent class models, the 4 latent class model has a lower AIC, BIC and aBIC, and the likelihood ratio statistical test (the Lo–Mendell–Rubin adjusted likelihood ratio test) confirmed it has a significantly better model fit. The 5 and 6 latent class models did not show a better model fit than the 4 latent class model. Therefore, based on the results, four qualitatively different exploration strategy profiles can be distinguished, which covered 96% of the students.
Fit indices for latent class analyses.
Number of Latent Classes | AIC | BIC | aBIC | Entropy | L–M–R Test | |
---|---|---|---|---|---|---|
2 | 9078 | 9333 | 9177 | 0.987 | 4255 | <0.001 |
3 | 8520 | 8905 | 8670 | 0.939 | 604 | <0.001 |
4 | 8381 | 8897 | 8582 | 0.959 | 188 | <0.05 |
5 | 8339 | 8984 | 8591 | 0.955 | 92 | 0.93 |
6 | 8309 | 9084 | 8611 | 0.877 | 96 | 0.34 |
The patterns for the four qualitatively different exploration strategy profiles are shown in Figure 6 . In total, 84.3% of the students were proficient exploration strategy users, who were able to use VOTAT in each problem scenario independent of its difficulty level (represented by the red line in Figure 5 ). In total, 6.2% of the students were rapid learners. They were not able to apply VOTAT at the beginning of the test on the easiest problems but managed to learn quickly, and, after a rapid learning curve by the end of the test, they reached the level of proficient exploration strategy users, even though the problems became much more complex (represented by the blue line). In total, 3.1% of the students proved to be non-persistent explorers, and they employed VOTAT on the easiest problems but did not transfer this knowledge to the more complex problems. Finally, they were no longer able to apply VOTAT when the complexity of the problems increased (represented by the green line). In total, 6.5% of the students were non-performing explorers; they barely used any VOTAT strategy during the whole test (represented by the pink line) independent of problem complexity.
Four qualitatively different exploration strategy profiles.
4.3. Better Exploration Strategy Users Showed Better Performance in Reasoning Skills
Students with different exploration strategy profiles showed different kinds of performance in each reasoning skill under investigation. Results (see Table 3 ) showed that more proficient strategy users tended to have higher achievement in all the domains assessed as well as in the two sub-dimensions in CPS (i.e., KAC and KAP; ANOVA: CPS: F(3, 1339) = 187.28, p < 0.001; KAC: F(3, 1339) = 237.15, p < 0.001; KAP: F(3, 1339) = 74.91, p < 0.001; IR: F(3, 1339) = 48.10, p < 0.001; CR: F(3, 1339) = 28.72, p < 0.001); specifically, students identified as “proficient exploration strategy users” achieved the highest level on the reasoning skills tests independent of the domains. On average, they were followed by rapid learners, non-persistent explorers and, finally, non-performing explorers. Tukey’s post hoc tests revealed more details on the performance differences of students with different exploration profiles in each of the domains being measured. Proficient strategy users proved to be significantly more skilled in each of the reasoning domains. They were followed by rapid learners, who outperformed non-persistent explorers and non-performing explorers in CPS. In the domains of IR and CR, there were no achievement differences between rapid learners and non-persistent explorers, who significantly outperformed non-performing strategy explorers.
Students’ performance on each test—grouped according to the different exploration strategy profiles.
Class Profiles | CPS | IR | CR | |||
---|---|---|---|---|---|---|
Overall | KAC | KAP | ||||
Proficient strategy users | Mean (%) | 61.37 | 69.57 | 53.17 | 67.79 | 70.47 |
S.D. (%) | 19.67 | 22.25 | 21.90 | 14.22 | 18.96 | |
Rapid learners | Mean (%) | 35.39 | 36.65 | 34.14 | 59.23 | 62.67 |
S.D. (%) | 14.26 | 20.45 | 17.15 | 14.22 | 17.60 | |
Non-persistent explorers | Mean (%) | 27.03 | 24.59 | 29.47 | 57.29 | 56.11 |
S.D. (%) | 10.75 | 14.06 | 11.80 | 18.75 | 24.52 | |
Non-performing explorers | Mean (%) | 22.75 | 19.64 | 25.86 | 50.65 | 53.72 |
S.D. (%) | 12.67 | 15.30 | 16.38 | 16.55 | 23.99 |
4.4. The Roles of IR and CR in CPS and Its Processes Were Different for Each Type of Exploration Strategy User
Path analysis was used to explore the predictive power of IR and CR for CPS and its processes, knowledge acquisition and knowledge application, for each group of students with different exploration strategy profiles. That is, four path analysis models were built to indicate the predictive power of IR and CR for CPS (see Figure 7 ), and another four path analyses models were developed to monitor the predictive power of IR and CR for the two empirically distinguishable phases of CPS (i.e., KAC and KAP) (see Figure 8 ). All eight models had good model fits, the fit indices TLI and CFI were above 0.90, and RMSEA was less than 0.08.
Path analysis models (with CPS, IR and CR) for each type of strategy user; * significant at 0.05 ( p < 0.05); ** significant at 0.01 ( p < 0.01); N.S.: no significant effect can be found.
Path analysis models (with KAC, KAP, IR and CR) for each type of strategy user; * significant at 0.05 ( p < 0.05); ** significant at 0.01 ( p < 0.01); N.S.: no significant effect can be found.
Students’ level of IR significantly predicted their level of CPS in all four path analysis models independent of their exploration strategy profile ( Figure 7 ; proficient strategy users: β = 0.432, p < 0.01; rapid learners: β = 0.350, p < 0.01; non-persistent explorers: β = 0.309, p < 0.05; and non-performing explorers: β = 0.386, p < 0.01). This was not the case for CR, which only proved to have predictive power for CPS among proficient strategy users (β = 0.104, p < 0.01). IR and CR were significantly correlated in all four models.
After examining the roles of IR and CR in the CPS process, we went further to explore the roles of these two reasoning skills in the distinguishable phases of CPS. The path analysis models ( Figure 8 ) showed that the predictive power of IR and CR for KAC and KAP was varied in each group. Levels of IR and CR among non-persistent explorers and non-performing explorers failed to predict their achievement in the KAC phase of the CPS process. Moreover, rapid learners’ level of IR significantly predicted their achievement in the KAC phase (β = 0.327, p < 0.01), but their level of CR did not have the same predictive power. Furthermore, the proficient strategy users’ levels of both reasoning skills had significant predictive power for KAC (IR: β = 0.363, p < 0.01; CR: β = 0.132, p < 0.01). In addition, in the KAP phase of the CPS problems, IR played a significant role for all types of strategy users, although with different power (proficient strategy users: β = 0.408, p < 0.01; rapid learners: β = 0.339, p < 0.01; non-persistent explorers: β = 0.361, p < 0.01; and non-performing explorers: β = 0.447, p < 0.01); by contrast, CR did not have significant predictive power for the KAP phase in any of the models.
5. Discussion
The study aims to investigate the role of IR and CR in CPS and its phases among students using statistically distinguishable exploration strategies in different CPS environments. We examined 1343 Hungarian university students and assessed their CPS, IR and CR skills. Both achievement data and logfile data were used in the analysis. The traditional achievement indicators formed the foundation for analysing the students’ CPS, CR and IR performance, whereas process data extracted from logfile data were used to explore students’ exploration behaviour in various CPS environments.
Four qualitatively different exploration strategy profiles were distinguished: proficient strategy users, rapid learners, non-persistent explorers and non-performing explorers (RQ1). The four profiles were consistent with the result of another study conducted at university level (see Molnár et al. 2022 ), and the frequencies of these four profiles in these two studies were very similar. The two studies therefore corroborate and validate each other’s results. The majority of the participants were identified as proficient strategy users. More than 80% of the university students were able to employ effective exploration strategies in various CPS environments. Of the remaining students, some performed poorly in exploration strategy use in the early part of the test (rapid learners), some in the last part (non-persistent explorers) and some throughout the test (non-performing explorers). However, students with these three exploration strategy profiles only constituted small portions of the total sample (with proportions ranging from 3.1% to 6.5%). The university students therefore exhibited generally good performance in terms of exploration strategy use in a CPS environment, especially compared with previous results among younger students (e.g., primary school students, see Greiff et al. 2018 ; Wu and Molnár 2021 ; primary to secondary students, see Molnár and Csapó 2018 ).
The results have indicated that better exploration strategy users achieved higher CPS performance and had better development levels of IR and CR (RQ2). First, the results have confirmed the importance of VOTAT in a CPS environment. This finding is consistent with previous studies (e.g., Greiff et al. 2015a ; Molnár and Csapó 2018 ; Mustafić et al. 2019 ; Wu and Molnár 2021 ). Second, the results have confirmed that effective use of VOTAT is strongly tied to the level of IR and CR development. Reasoning forms an important component of human intelligence, and the level of development in reasoning was an indicator of the level of intelligence ( Klauer et al. 2002 ; Sternberg and Kaufman 2011 ). Therefore, this finding has supplemented empirical evidence for the argument that effective use of VOTAT is associated with levels of intelligence to a certain extent.
The roles of IR and CR proved to be varied for each type of exploration strategy user (RQ3). For instance, the level of CPS among the best exploration strategy users (i.e., the proficient strategy users) was predicted by both the levels of IR and CR, but this was not the case for students with other profiles. In addition, the results have indicated that IR played important roles in both the KAC and KAP phases for the students with relatively good exploration strategy profiles (i.e., proficient strategy users and rapid learners) but only in the KAP phase for the rest of the students (non-persistent explorers and non-performing explorers); moreover, the predictive power of CR can only be detected in the KAC phase of the proficient strategy users. To sum up, the results suggest a general trend of IR and CR playing more important roles in the CPS process among better exploration strategy users.
Combining the answers to RQ2 and RQ3, we can gain further insights into students’ exploration strategy use in a CPS environment. Our results have confirmed that the use of VOTAT is associated with the level of IR and CR development and that the importance of IR and CR increases with proficiency in exploration strategy use. Based on these findings, we can make a reasonable argument that IR and CR are essential skills for using VOTAT and that underdeveloped IR and CR will prevent students from using effective strategies in a CPS environment. Therefore, if we want to encourage students to become better exploration strategy users, it is important to first enhance their IR and CR skills. Previous studies have suggested that establishing explicit training in using effective strategies in a CPS environment is important for students’ CPS development ( Molnár et al. 2022 ). Our findings have identified the importance of IR and CR in exploration strategy use, which has important implications for designing training programmes.
The results have also provided a basis for further studies. Future studies have been suggested to further link the behavioural and cognitive perspectives in CPS research. For instance, IR and CR were considered as component skills of CPS (see Section 1.2 ). The results of the study have indicated the possibility of not only discussing the roles of IR and CR in the cognitive process of CPS, but also exploration behaviour in a CPS environment. The results have thus provided a new perspective for exploring the component skills of CPS.
6. Limitations
There are some limitations in the study. All the tests were low stake; therefore, students might not be sufficiently motivated to do their best. This feature might have produced the missing values detected in the sample. In addition, some students’ exploration behaviour shown in this study might theoretically be below their true level. However, considering that data cleaning was adopted in this study (see Section 3.1 ), we believe this phenomenon will not have a remarkable influence on the results. Moreover, the CPS test in this study was based on the MicroDYN approach, which is a well-established and widely used artificial model with a limited number of variables and relations. However, it does not have the power to cover all kinds of complex and dynamic problems in real life. For instance, the MicroDYN approach cannot measure ill-defined problem solving. Thus, this study can only demonstrate the influence of IR and CR on problem solving in well-defined MicroDYN-simulated problems. Furthermore, VOTAT is helpful with minimally complex problems under well-defined laboratory conditions, but it may not be that helpful with real-world, ill-defined complex problems ( Dörner and Funke 2017 ; Funke 2021 ). Therefore, the generalizability of the findings is limited.
7. Conclusions
In general, the results have shed new light on students’ problem-solving behaviours in respect of exploration strategy in a CPS environment and explored differences in terms of the use of thinking skills between students with different exploration strategies. Most studies discuss students’ problem-solving strategies from a behavioural perspective. By contrast, this paper discusses them from both behavioural and cognitive perspectives, thus expanding our understanding in this area. As for educational implications, the study contributes to designing and revising training methods for CPS by identifying the importance of IR and CR in exploration behaviour in a CPS environment. To sum up, the study has investigated the nature of CPS from a fresh angle and provided a sound basis for future studies.
Funding Statement
This study has been conducted with support provided by the National Research, Development and Innovation Fund of Hungary, financed under the OTKA K135727 funding scheme and supported by the Research Programme for Public Education Development, Hungarian Academy of Sciences (KOZOKT2021-16).
Author Contributions
Conceptualization, H.W. and G.M.; methodology, H.W. and G.M.; formal analysis, H.W.; writing—original draft preparation, H.W.; writing—review and editing, G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
Ethical approval was not required for this study in accordance with the national and institutional guidelines. The assessments which provided data for this study were integrated parts of the educational processes of the participating university. The participation was voluntary.
Informed Consent Statement
All of the students in the assessment turned 18, that is, it was not required or possible to request and obtain written informed parental consent from the participants.
Data Availability Statement
Conflicts of interest.
Authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- Adey Philip, Csapó Benő. Developing and Assessing Scientific Reasoning. In: Csapó Benő, Szabó Gábor., editors. Framework for Diagnostic Assessment of Science. Nemzeti Tankönyvkiadó; Budapest: 2012. pp. 17–53. [ Google Scholar ]
- Batanero Carmen, Navarro-Pelayo Virginia, Godino Juan D. Effect of the implicit combinatorial model on combinatorial reasoning in secondary school pupils. Educational Studies in Mathematics. 1997; 32 :181–99. doi: 10.1023/A:1002954428327. [ CrossRef ] [ Google Scholar ]
- Beckmann Jens F., Guthke Jürgen. Complex problem solving, intelligence, and learning ability. In: Frensch Peter A., Funke Joachim., editors. Complex Problem Solving: The European Perspective. Erlbaum; Hillsdale: 1995. pp. 177–200. [ Google Scholar ]
- Buchner Axel. Basic topics and approaches to the study of complex problem solving. In: Frensch Peter A., Funke Joachim., editors. Complex Problem Solving: The European Perspective. Erlbaum; Hillsdale: 1995. pp. 27–63. [ Google Scholar ]
- Chen Yunxiao, Li Xiaoou, Liu Jincheng, Ying Zhiliang. Statistical analysis of complex problem-solving process data: An event history analysis approach. Frontiers in Psychology. 2019; 10 :486. doi: 10.3389/fpsyg.2019.00486. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Csapó Benő. A kombinatív képesség struktúrája és fejlődése. Akadémiai Kiadó; Budapest: 1988. [ Google Scholar ]
- Csapó Benő. The development of inductive reasoning: Cross-sectional assessments in an educational context. International Journal of Behavioral Development. 1997; 20 :609–26. doi: 10.1080/016502597385081. [ CrossRef ] [ Google Scholar ]
- Csapó Benő. Teaching and Learning Thinking Skills. Swets & Zeitlinger; Lisse: 1999. Improving thinking through the content of teaching; pp. 37–62. [ Google Scholar ]
- Csapó Benő, Molnár Gyöngyvér. Online diagnostic assessment in support of personalized teaching and learning: The eDia System. Frontiers in Psychology. 2019; 10 :1522. doi: 10.3389/fpsyg.2019.01522. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Dörner Dietrich, Funke Joachim. Complex problem solving: What it is and what it is not. Frontiers in Psychology. 2017; 8 :1153. doi: 10.3389/fpsyg.2017.01153. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- English Lyn D. Combinatorics and the development of children’s combinatorial reasoning. In: Jones Graham A., editor. Exploring Probability in School: Challenges for Teaching and Learning. Springer; New York: 2005. pp. 121–41. [ Google Scholar ]
- Fischer Andreas, Greiff Samuel, Funke Joachim. The process of solving complex problems. Journal of Problem Solving. 2012; 4 :19–42. doi: 10.7771/1932-6246.1118. [ CrossRef ] [ Google Scholar ]
- Frensch Peter A., Funke Joachim. Complex Problem Solving: The European Perspective. Psychology Press; New York: 1995. [ Google Scholar ]
- Funke Joachim. Dynamic systems as tools for analysing human judgement. Thinking and Reasoning. 2001; 7 :69–89. doi: 10.1080/13546780042000046. [ CrossRef ] [ Google Scholar ]
- Funke Joachim. Complex problem solving: A case for complex cognition? Cognitive Processing. 2010; 11 :133–42. doi: 10.1007/s10339-009-0345-0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Funke Joachim. It Requires More Than Intelligence to Solve Consequential World Problems. Journal of Intelligence. 2021; 9 :38. doi: 10.3390/jintelligence9030038. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Funke Joachim, Fischer Andreas, Holt Daniel V. Competencies for complexity: Problem solving in the twenty-first century. In: Care Esther, Griffin Patrick, Wilson Mark., editors. Assessment and Teaching of 21st Century Skills. Springer; Dordrecht: 2018. pp. 41–53. [ Google Scholar ]
- Gilhooly Kenneth J. Thinking: Directed, Undirected and Creative. Academic Press; London: 1982. [ Google Scholar ]
- Gnaldi Michela, Bacci Silvia, Kunze Thiemo, Greiff Samuel. Students’ complex problem solving profiles. Psychometrika. 2020; 85 :469–501. doi: 10.1007/s11336-020-09709-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Greiff Samuel, Funke Joachim. Measuring complex problem solving-the MicroDYN approach. In: Scheuermann Friedrich, Björnsson Julius., editors. The Transition to Computer-Based Assessment. Office for Official Publications of the European Communities; Luxembourg: 2009. pp. 157–63. [ Google Scholar ]
- Greiff Samuel, Holt Daniel V., Funke Joachim. Perspectives on problem solving in educational assessment: Analytical, interactive, and collaborative problem solving. Journal of Problem Solving. 2013; 5 :71–91. doi: 10.7771/1932-6246.1153. [ CrossRef ] [ Google Scholar ]
- Greiff Samuel, Molnár Gyöngyvér, Martina Romain, Zimmermann Johannes, Csapó Benő. Students’ exploration strategies in computer-simulated complex problem environments: A latent class approach. Computers & Education. 2018; 126 :248–63. [ Google Scholar ]
- Greiff Samuel, Wüstenberg Sascha, Avvisati Francesco. Computer-generated log-file analyses as a window into students’ minds? A showcase study based on the PISA 2012 assessment of problem solving. Computers & Education. 2015a; 91 :92–105. [ Google Scholar ]
- Greiff Samuel, Wüstenberg Sascha, Funke Joachim. Dynamic problem solving: A new measurement perspective. Applied Psychological Measurement. 2012; 36 :189–213. doi: 10.1177/0146621612439620. [ CrossRef ] [ Google Scholar ]
- Greiff Samuel, Wüstenberg Sascha, Csapó Benő, Demetriou Andreas, Hautamäki Jarkko, Graesser Arthur C., Martin Romain. Domain-general problem solving skills and education in the 21st century. Educational Research Review. 2014; 13 :74–83. doi: 10.1016/j.edurev.2014.10.002. [ CrossRef ] [ Google Scholar ]
- Greiff Samuel, Wüstenberg Sascha, Goetz Thomas, Vainikainen Mari-Pauliina, Hautamäki Jarkko, Bornstein Marc H. A longitudinal study of higher-order thinking skills: Working memory and fluid reasoning in childhood enhance complex problem solving in adolescence. Frontiers in Psychology. 2015b; 6 :1060. doi: 10.3389/fpsyg.2015.01060. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Hołda Małgorzata, Głodek Anna, Dankiewicz-Berger Malwina, Skrzypińska Dagna, Szmigielska Barbara. Ill-defined problem solving does not benefit from daytime napping. Frontiers in Psychology. 2020; 11 :559. doi: 10.3389/fpsyg.2020.00559. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Klauer Karl Josef. Paradigmatic teaching of inductive thinking. Learning and Instruction. 1990; 2 :23–45. [ Google Scholar ]
- Klauer Karl Josef, Willmes Klaus, Phye Gary D. Inducing inductive reasoning: Does it transfer to fluid intelligence? Contemporary Educational Psychology. 2002; 27 :1–25. doi: 10.1006/ceps.2001.1079. [ CrossRef ] [ Google Scholar ]
- Kuhn Deanna. What is scientific thinking and how does it develop? In: Goswami Usha., editor. The Wiley-Blackwell Handbook of Childhood Cognitive Development. Wiley-Blackwell; Oxford: 2010. pp. 371–93. [ Google Scholar ]
- Kuhn Deanna, Garcia-Mila Merce, Zohar Anat, Andersen Christopher, Sheldon H. White, Klahr David, Carver Sharon M. Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development. 1995; 60 :1–157. doi: 10.2307/1166059. [ CrossRef ] [ Google Scholar ]
- Lo Yungtai, Mendell Nancy R., Rubin Donald B. Testing the number of components in a normal mixture. Biometrika. 2001; 88 :767–78. doi: 10.1093/biomet/88.3.767. [ CrossRef ] [ Google Scholar ]
- Lotz Christin, Scherer Ronny, Greiff Samuel, Sparfeldt Jörn R. Intelligence in action—Effective strategic behaviors while solving complex problems. Intelligence. 2017; 64 :98–112. doi: 10.1016/j.intell.2017.08.002. [ CrossRef ] [ Google Scholar ]
- Mayer Richard E. Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science. 1998; 26 :49–63. doi: 10.1023/A:1003088013286. [ CrossRef ] [ Google Scholar ]
- Molnár Gyöngyvér, Csapó Benő. Az 1–11 évfolyamot átfogó induktív gondolkodás kompetenciaskála készítése a valószínűségi tesztelmélet alkalmazásával. Magyar Pedagógia. 2011; 111 :127–40. [ Google Scholar ]
- Molnár Gyöngyvér, Csapó Benő. The efficacy and development of students’ problem-solving strategies during compulsory schooling: Logfile analyses. Frontiers in Psychology. 2018; 9 :302. doi: 10.3389/fpsyg.2018.00302. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Molnár Gyöngyvér, Alrababah Saleh Ahmad, Greiff Samuel. How we explore, interpret, and solve complex problems: A cross-national study of problem-solving processes. Heliyon. 2022; 8 :e08775. doi: 10.1016/j.heliyon.2022.e08775. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Molnár Gyöngyvér, Greiff Samuel, Csapó Benő. Inductive reasoning, domain specific and complex problem solving: Relations and development. Thinking Skills and Creativity. 2013; 9 :35–45. doi: 10.1016/j.tsc.2013.03.002. [ CrossRef ] [ Google Scholar ]
- Mousa Mojahed, Molnár Gyöngyvér. Computer-based training in math improves inductive reasoning of 9- to 11-year-old children. Thinking Skills and Creativity. 2020; 37 :100687. doi: 10.1016/j.tsc.2020.100687. [ CrossRef ] [ Google Scholar ]
- Mustafić Maida, Yu Jing, Stadler Matthias, Vainikainen Mari-Pauliina, Bornstein Marc H., Putnick Diane L., Greiff Samuel. Complex problem solving: Profiles and developmental paths revealed via latent transition analysis. Developmental Psychology. 2019; 55 :2090–101. doi: 10.1037/dev0000764. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Muthén Linda K., Muthén Bengt O. Mplus User’s Guide. Muthén & Muthén; Los Angeles: 2010. [ Google Scholar ]
- Newell Allen. Reasoning, Problem Solving, and Decision Processes: The Problem Space as a Fundamental Category. MIT Press; Boston: 1993. [ Google Scholar ]
- Novick Laura R., Bassok Miriam. Problem solving. In: Holyoak Keith James, Morrison Robert G., editors. The Cambridge Handbook of Thinking and Reasoning. Cambridge University Press; New York: 2005. pp. 321–49. [ Google Scholar ]
- OECD . PISA 2012 Field Trial Problem Solving Framework. OECD Publishing; Paris: 2010. [ Google Scholar ]
- OECD . Results: Creative Problem Solving—Students’ Skills in Tackling Real-Life Problems (Volume V) OECD Publishing; Paris: 2014. [ Google Scholar ]
- Pásztor Attila. Ph.D. thesis. Doctoral School of Education, University of Szeged; Szeged, Hungary: 2016. Technology-Based Assessment and Development of Inductive Reasoning. [ Google Scholar ]
- Pásztor Attila, Csapó Benő. Improving Combinatorial Reasoning through Inquiry-Based Science Learning; Paper presented at the Science and Mathematics Education Conference; Dublin, Ireland. June 24–25; 2014. [ Google Scholar ]
- Pásztor Attila, Kupiainen Sirkku, Hotulainen Risto, Molnár Gyöngyvér, Csapó Benő. Comparing Finnish and Hungarian Fourth Grade Students’ Inductive Reasoning Skills; Paper presented at the EARLI SIG 1 Conference; Helsinki, Finland. August 29–31; 2018. [ Google Scholar ]
- Sandberg Elisabeth Hollister, McCullough Mary Beth. The development of reasoning skills. In: Sandberg Elisabeth Hollister, Spritz Becky L., editors. A Clinician’s Guide to Normal Cognitive Development in Childhood. Routledge; New York: 2010. pp. 179–89. [ Google Scholar ]
- Schraw Gregory, Dunkle Michael E., Bendixen Lisa D. Cognitive processes in well-defined and ill-defined problem solving. Applied Cognitive Psychology. 1995; 9 :523–38. doi: 10.1002/acp.2350090605. [ CrossRef ] [ Google Scholar ]
- Schweizer Fabian, Wüstenberg Sascha, Greiff Samuel. Validity of the MicroDYN approach: Complex problem solving predicts school grades beyond working memory capacity. Learning and Individual Differences. 2013; 24 :42–52. doi: 10.1016/j.lindif.2012.12.011. [ CrossRef ] [ Google Scholar ]
- Stadler Matthias, Becker Nicolas, Gödker Markus, Leutner Detlev, Greiff Samuel. Complex problem solving and intelligence: A meta-analysis. Intelligence. 2015; 53 :92–101. doi: 10.1016/j.intell.2015.09.005. [ CrossRef ] [ Google Scholar ]
- Sternberg Robert J. Handbook of Human Intelligence. Cambridge University Press; New York: 1982. [ Google Scholar ]
- Sternberg Robert J., Kaufman Scott Barry. The Cambridge Handbook of Intelligence. Cambridge University Press; New York: 2011. [ Google Scholar ]
- van de Schoot Rens, Lugtig Peter, Hox Joop. A checklist for testing measurement invariance. European Journal of Developmental Psychology. 2012; 9 :486–92. doi: 10.1080/17405629.2012.686740. [ CrossRef ] [ Google Scholar ]
- Vollmeyer Regina, Burns Bruce D., Holyoak Keith J. The impact of goal specificity on strategy use and the acquisition of problem structure. Cognitive Science. 1996; 20 :75–100. doi: 10.1207/s15516709cog2001_3. [ CrossRef ] [ Google Scholar ]
- Welter Marisete Maria, Jaarsveld Saskia, Lachmann Thomas. Problem space matters: The development of creativity and intelligence in primary school children. Creativity Research Journal. 2017; 29 :125–32. doi: 10.1080/10400419.2017.1302769. [ CrossRef ] [ Google Scholar ]
- Wenke Dorit, Frensch Peter A., Funke Joachim. Complex Problem Solving and intelligence: Empirical relation and causal direction. In: Sternberg Robert J., Pretz Jean E., editors. Cognition and Intelligence: Identifying the Mechanisms of the Mind. Cambridge University Press; New York: 2005. pp. 160–87. [ Google Scholar ]
- Wittmann Werner W., Hattrup Keith. The relationship between performance in dynamic systems and intelligence. Systems Research and Behavioral Science. 2004; 21 :393–409. doi: 10.1002/sres.653. [ CrossRef ] [ Google Scholar ]
- Wu Hao, Molnár Gyöngyvér. Interactive problem solving: Assessment and relations to combinatorial and inductive reasoning. Journal of Psychological and Educational Research. 2018; 26 :90–105. [ Google Scholar ]
- Wu Hao, Molnár Gyöngyvér. Logfile analyses of successful and unsuccessful strategy use in complex problem-solving: A cross-national comparison study. European Journal of Psychology of Education. 2021; 36 :1009–32. doi: 10.1007/s10212-020-00516-y. [ CrossRef ] [ Google Scholar ]
- Wu Hao, Saleh Andi Rahmat, Molnár Gyöngyvér. Inductive and combinatorial reasoning in international educational context: Assessment, measurement invariance, and latent mean differences. Asia Pacific Education Review. 2022; 23 :297–310. doi: 10.1007/s12564-022-09750-z. [ CrossRef ] [ Google Scholar ]
- Wüstenberg Sascha, Greiff Samuel, Funke Joachim. Complex problem solving—More than reasoning? Intelligence. 2012; 40 :1–14. doi: 10.1016/j.intell.2011.11.003. [ CrossRef ] [ Google Scholar ]
- Wüstenberg Sascha, Greiff Samuel, Molnár Gyöngyvér, Funke Joachim. Cross-national gender differences in complex problem solving and their determinants. Learning and Individual Differences. 2014; 29 :18–29. doi: 10.1016/j.lindif.2013.10.006. [ CrossRef ] [ Google Scholar ]
Westside Toastmasters is located in Los Angeles and Santa Monica, California
Chapter 6. the parts of thinking.
One of the most important sets of skills in thinking develops through one's understanding of the parts of thinking. In other words, we are better able to find problems in our thinking when we are able to take our thinking apart. In this chapter, we focus on these parts. In the next chapter, we focus on intellectual standards, the key to the assessment of thinking.
Thus, as you work through this chapter and the next, you will begin to understand some of the most fundamental concepts critical thinkers use on a daily basis, for it is through the analysis and assessment of thinking that critical thinking occurs. To analyze thinking we must be able to take thinking apart and scrutinize how we are using each part. Once we have done so, we apply the standards for thinking to those parts (standards such as clarity, accuracy, relevance, logicalness, fairness, etc.). Once we have a clear understanding of the parts of thinking (or elements of reasoning) and the intellectual standards, and once we begin to use them in our thinking on a daily basis, we begin to see the quality of our lives significantly improve.
Figure 6.1. Critical thinkers routinely apply the intellectual standards to the elements of reasoning in order to develop intellectual traits.
Here we begin with a brief discussion of reasoning, the mental process the mind uses to make sense of whatever we seek to understand.
Reasoning Is Everywhere in Human Life
The words thinking and reasoning are used in everyday life as virtual synonyms. Reasoning, however, has a more formal flavor. This is because it highlights the intellectual dimension of thinking.
Reasoning occurs whenever the mind draws conclusions on the basis of reasons. We draw conclusions whenever we make sense of things. The result is that whenever we think, we reason. Usually we are not aware of the full scope of reasoning in our lives.
We begin to reason from the moment we wake up in the morning. We reason when we figure out what to eat for breakfast, what to wear, whether to stop at the store on the way to school, whether to go with this or that friend to lunch. We reason as we interpret the oncoming flow of traffic, when we react to the decisions of other drivers, when we speed up or slow down. We reason when we figure out solutions to problems. We reason when we formulate problems. We reason when we argue.
One can draw conclusions, then, about everyday events or, really, about anything at all: about strategic planning, newspaper articles, poems, microbes, people, numbers, historical events, social settings, psychological states, character traits, the past, the present, or the future.
To reason well, we must scrutinize the process we are using. What are we trying to figure out? What information do we need? Do we have that information? How could we check it for accuracy? The less conscious we are of how we are thinking, the easier it is to make some mistake or error.
Make a list of all the things you did today. Then, for each act, figure out the thinking that led you to do, or guided you while doing, the act. (Remember that most of your thinking is unconscious.) For example, when you left your house this morning, you may have stopped at the store for food. This act makes no sense unless you somehow had come to the conclusion that you needed some food. Then, while at the store, you bought a certain number of items. This action resulted from the tacit conclusion you came to that you needed some items and not others. Realize that every time you make a decision, that decision represents a view or conclusion you reasoned to. For each action you identify, answer these two questions: 1) What exactly did I do? and 2) What thinking is presupposed in my behavior? |
Does Reasoning Have Parts?
The parts of thinking can also be called the elements of reasoning or the fundamental structures of thought. We will use these expressions interchangeably. The elements or parts of reasoning are those essential dimensions of reasoning that are present whenever and wherever reasoning occurs - independent of whether we are reasoning well or poorly ( Figure 6.2 ). Working together, these elements shape reasoning and provide a general logic to the use of thought.
Figure 6.2. These parts or elements of reasoning are always present in human thinking.
When we become adept at identifying the elements of our reasoning ( Figure 6.3 ), we are in a much better position to recognize flaws in our thinking, by locating problems in this or that part. We are in a much better position, in other words, to analyze the mistakes in our thinking (or mistakes in the thinking of others).
Figure 6.3. Critical thinkers understand the importance of taking thinking apart in order to analyze it for flaws.
Beginning to think about your own reasoning.
Reasoning is a process whereby one draws conclusions on the basis of reasons. On the surface, reasoning seems somewhat simple, as if it has no component structures. Looked at more closely, however, it implies the ability to engage in a set of interrelated intellectual processes.
It is useful to practice making conscious what is subconscious in your thinking. Then you can better understand what's going on beneath the surface of your thought. In this chapter, we introduce you to important ideas you can use for this task.
The Elements of Thought: A First Look
Let us begin by looking at the parts of thinking as they stand in an interrelated set. It is possible to name them in just one, somewhat complex, sentence:
Whenever you reason, you do so in some circumstances, making some inferences (that have some implications and consequences) based on some reasons or information (and assumptions) using some concepts, in trying to settle some question (or solve some problem) for some purpose within a point of view.
If you like, you can put it in two sentences (also see Figure 6.4 ):
Whenever you are reasoning, you are trying to accomplish some purpose, within a point of view, using concepts or ideas. You are focused on some issue or question, issue, or problem, using information to come to conclusions, based on assumptions, all of which have implications.
Figure 6.4. If you understand the parts of thinking, you can ask the crucial questions implied by those parts.
Let us now examine, at least provisionally, each of these crucial concepts. We will be using them throughout this resource. It is essential that they become a comfortable part of your vocabulary. As you read these explanations, see if you can write out your understanding of them, with an example drawn from your own experience.
By reasoning, we mean making sense of something by giving it some meaning in one's mind. Virtually all thinking is part of our sense-making activities. We hear scratching at the door and think, "It's the dog." We see dark clouds in the sky and think, "It looks like rain." Some of this activity operates at a subconscious level. For example, all of the sights and sounds about me have meaning for me without my explicitly noticing that they do. Most of our reasoning is unspectacular. Our reasoning tends to become explicit to us only when someone challenges it and we have to defend it. ("Why do you say that Jack is obnoxious? I thought he was quite pleasant."). Throughout life, we begin with a goal or purpose and then figure out what to do to achieve our goal. Reasoning is what enables us to come to these decisions using ideas and meanings.
By reasoning having a purpose, we mean that when humans think about the world, we do not do so randomly but, rather, in line with our goals, desires, needs, and values. Our thinking is an integral part of a patterned way of acting in the world, and we act, even in simple matters, with some set of ends in view. To understand someone's thinking - including one's own - we must understand the functions it serves, what it is about, the direction it is moving, and the ends that make sense of it. Most of what we are after in our thinking is not obvious to us, though. Raising human goals and desires to the level of conscious realization is an important part of critical thinking.
By reasoning within a point of view, we mean that our thinking has some comprehensive focus or orientation. Our thinking is focused on something from some angle. We can change either what we focus on or the angle of our focus. We often give names to the angle from which we are thinking about something. For example, we could look at something politically or scientifically, poetically or philosophically. We might look at something conservatively or liberally, religiously or secularly. We might look at something from a cultural or a financial perspective, or both. Once we understand how people are approaching a question or topic (what their comprehensive perspective is), we are usually much more able to understand the whole of their thinking.
By using concepts in reasoning, we mean the general categories or ideas by which we interpret, classify, or group the information we use in our thinking. For example, in this resource the concepts of critical thinking and uncritical thinking are important. Everything written in this resource can be classified as an attempt to explain one or the other of these two important ideas. Each of these ideas is explained, in turn, by means of other ideas. Thus, the concept of thinking critically is explained by reference to yet other concepts such as "intellectual standards for thought." Each profession or discipline (business, psychology, science, geology, literature, history) develops its own set of concepts or technical vocabulary to facilitate its thinking. All sports require a vocabulary of concepts that enables those who are trying to understand or master the game to make sense of it. Try to explain baseball to someone without using these ideas: strike, ball, shortstop, inning, at bat, hit, run, safe, out, balk. To play the game, we must interpret everything we do in it by means of concepts such as these. The rules would not make sense without them. The game would be incomprehensible.
By reasoning upon some question, issue, or problem, we mean that when we think about the world in line with our goals, desires, needs, and values, we often face questions we need to answer, problems we need to solve, and issues we need to resolve. Therefore, when we find ourselves confronting a difficulty, it makes sense to say, "What is the question we need to answer?" or, "What is the problem we need to solve?" or, "What is the issue we need to resolve?" To improve our ability to think well, it is important to learn how to put the questions, problems, and issues we need to deal with in a clear and distinct way. If we change the question, we change the criteria we have to meet to settle it. If we modify the problem, we need to modify how we are going to solve the problem. If we shift the issue, new considerations become relevant to its resolution.
By using information in our reasoning, we mean using some set of facts, data, or experiences to support our conclusions. Whenever someone is reasoning, it makes sense to ask, "Upon what facts or information are you basing your reasoning?" The factual basis for reasoning can be important. For example, in a media ad, the following pieces of information were used in support of an argument against capital punishment:
"Since the death penalty was reinstated by the Supreme court in 1976, for every 7 prisoners who were executed, one prisoner awaiting execution was found to be innocent and released." "At least 381 homicide convictions have been overturned since 1963 because prosecutors concealed evidence of innocence or presented evidence they knew to be false." "A study by the U.S. General Accounting Office found racial prejudice in death sentencing...: killers of whites were proportionally more likely to be executed than were killers of blacks." "Since 1984, 34 mentally retarded people have been executed. (New York Times, November 22, 1999).-->"
Can you see how information such as this - if true - gives strength to the reasoning? The opposing position would, of course, advance information of its own to try to challenge or counter this information. Two important critical thinking axioms are: check your facts and check your data!
By coming to conclusions we mean taking something (which we believe we know) and figuring out something else on the basis of it. When we do this, we make inferences. For example, if my boss walks right by me without saying hello, I might come to the conclusion (make the inference) that he or she is angry with me. If the market goes up for six straight months, I might infer that it will go up again in the next month. If my business was successful with a strategy last year, I might infer that it will work again next year. In everyday life, we are continually making inferences (coming to conclusions) about the people, things, places, and events of our lives.
By reasoning based on assumptions we mean whatever we take for granted as true in order to figure something else out. Thus, if you infer that since a candidate is a Republican, he or she will support a balanced budget, you assume that all Republicans support a balanced budget. If you infer that foreign leaders presented in the news as "enemies" or "friends" of the U.S. are in fact enemies or friends, you assume that the news in the U.S. is always accurate in its presentation of the character of foreign leaders. If you infer that someone who invites you to their apartment after a party "to continue this interesting conversation" is really interested in you romantically or sexually, you assume that the only reason for going to someone's apartment late at night after a party is to pursue a romantic or sexual relationship. All reasoning has some basis in the assumptions we make (but usually do not openly express).
By the implications of reasoning, we mean that which follows from our thinking. It means that to which our thinking is leading us. If you say to someone that you "love" him, you imply that you are concerned with his welfare. If you make a promise, you imply that you intend to keep it. If you call a country a "democracy," you imply that the political power is in the hands of the people at large (as against in the hands of a powerful minority). If you call yourself a "feminist," you imply that you are in favor of the political, social, and economic equality of the sexes. We often test the credibility of people by seeing if they are true to the implications of their own words. "Say what you mean and mean what you say" is a sound principle of critical thinking (and of personal integrity, for that matter).
An Everyday Example: Jack and Jill
Let's now look at, and then analyze, a disagreement that might arise in everyday life - in this case, between lovers who come to different conclusions about a situation they both experienced.
Suppose Jack and Jill, who are in a romantic relationship, go to a party, during which Jack spends most of the evening talking with Susan. On their way back, Jack, sensing that Jill is upset, asks, "What's wrong?"
After some hesitation, Jill says, "I didn't appreciate your spending the whole night flirting with Susan!"
Select an important conclusion that you have reasoned to - for example, a decision to purchase a house or car or take a new job, or even to get married. Identify the circumstances in which you made that decision, some of the inferences you made in the process (about the likely advantages and disadvantages). State the likely implications of your decision, the consequences it has had, and will have, in your life, the information you took into account in making this decision, the way you expressed the question to yourself, the way you looked at your life and your future (while reasoning through the question). See if you can grasp the interrelationship of all of these elements in your thinking. Don't be surprised if you find this to be a difficult task. |
In the remainder of this chapter, we will give a more detailed account of concepts, assumptions, inferences, implications, and point of view. We will direct special attention to the distinction between inferences and assumptions, as we find that people often have difficulty distinguishing these two. But once you become comfortable differentiating these two elements, the others tend to fall into place much more readily. Light is shed on all the elements throughout this resource. Periodically put down the book and see if you can elaborate on the elements of thought in your own words using your own examples. Success in these acts of active elaboration are what will make the concepts yours. You must talk ideas, write ideas, think ideas into your system.
The Relationship Between the Elements
Because the elements do not exist in isolation but in relation to each other, it is important not to think of the distinctions between them as absolute. The distinctions are always a relative matter. For example, if our purpose is to figure out how to spend less money, the question we have to figure out is, "What can I do to ensure that I spend less money?" The question is a virtual reformulation of the purpose. What is more, the point of view might be expressed as "viewing my spending habits to determine how to decrease my expenditures." This seems a virtual reformulation of purpose and question. The point is that it is important to recognize an intimate overlap among all of the elements by virtue of their interrelationship. At times, formulating some of the elements explicitly may seem to be a redundancy. Don't give way to this feeling. With practice, you will come to recognize the analytic power of making the distinctions explicit.
Thinking to Some Purpose
A British scholar by the name of Susan Stebbing wrote a book (1939) on the importance of purpose in thinking. In it, she said: "To think logically is to think relevantly to the purpose that initiated the thinking: all effective thinking is directed to an end." We agree. All thinking pursues a purpose. We do not think without having something we are trying to accomplish, without having some aim in view, something we want. When humans think about the world, we do not do so randomly but, rather, in line with our goals, desires, needs, and values. Our thinking is an integral part of a patterned way of acting in the world, and we act, even in simple matters, with some set of ends in view. To understand someone's thinking - including one's own - we must understand the functions it serves, what it is about, the direction it is moving, and the ends that make sense of it.
Much of what we are after in our thinking is not obvious to us. Raising human goals and desires to the level of conscious realization is an important part of critical thinking. Though we always have a purpose in thinking, we are not always fully aware of that purpose. We may have some vague idea of it. Perhaps we have not clearly come to terms with our purpose. For example, you might call a meeting to discuss an important issue with your staff, but you may not know exactly what you are trying to accomplish in the meeting. As a result, the thinking during the meeting may diverge in many unhelpful directions. Without a clear sense of what you are about, the thinking you do may be very unproductive.
One problem with human thinking is that we sometimes pursue contradictory ends. We might want to become educated and also want to avoid doing any intellectual work. We might want others to love us, but not behave in loving ways toward them. We might want people to trust us, but behave in ways that undermine trust. The purpose we might explicitly state may be simply what we would like to believe of ourselves. Our real purpose, however, might be one that we would be ashamed to admit. We might think we want to pursue a medical career to help and care for people when our actual purpose may be to make a lot of money, gain prestige and status, and be admired by others. We must be careful, therefore, not to assume that our purposes are consistent with one another or that our announced purposes are our actual purposes.
Also, the purposes we pursue influence and are influenced by our point of view, as well as by the way we see the world. Our purposes shape how we see things, and how we see things shapes what we seek. Each person formulates his or her purpose from a given point of view, determined by the context of his or her own experience. To understand our goals and objectives, then, we should consider the perspectives from which we see the world or some situation in it.
A hairdresser, for example, because of her perspective, might be more concerned than most janitors with personal appearance. Looking good and helping others to look good are more intimately connected with her view of herself and the world. An orthodontist would naturally think much more about teeth and their appearance than most other people would. Having straight teeth would naturally seem more significant to her than it might to, say, most professional football players. The orthodontist's purpose in fostering straight teeth arises out of her perspective or point of view
To begin to see how intimately interconnected thinking is to purpose, we suggest the following activity. First, make a list of five fundamental goals you have. Then comment on how your thinking is shaped by those goals. Fill in the blanks: "One of my purposes is _______________. I can achieve this purpose best by ________________." Second, identify five things that you think about a lot. Then comment on how those things are tied to your fundamental purposes. For example, if you spend a considerable amount of time thinking about how to improve your performance at work in order to make more money, one of your purposes is probably to make as much money as you can. Or if you spend a lot of time thinking about how to improve your intimate relationship, one of your purposes is probably to have a more meaningful intimate relationship. |
Thinking with Concepts
Concepts are like the air we breathe. They are everywhere. They are essential to our life, but we rarely notice them. Yet only when we have conceptualized a thing in some way can we think about it. Nature does not give us instruction in how things are to be conceptualized. We must create that conceptualization, alone or with others. Once it is conceptualized, we integrate a thing into a network of ideas (as no concept stands alone).
Humans approach virtually everything in our experience as something that can be "decoded." Things are given meaning by the power of our mind to create a conceptualization and to make inferences on the basis of it - hence, we create further conceptualizations. We do this so routinely and automatically that we don't typically recognize ourselves as engaged in these processes. In our everyday life, we don't first experience the world in "concept-less" form and then deliberately place what we experience into categories so as to make sense of things. Rather, it is as if things are given to us with their name inherent in them. So we see trees, clouds, grass, roads, people, children, sunsets, and so on. We apply these concepts intuitively, as if the names belong to the things by nature, as if we had not created these concepts in our own minds.
If you want to develop as a thinker, you must come to terms with this human power of mind - to create concepts through which we see and experience the world - for it is precisely this capacity of which you must take charge in taking command of your thinking. You must become the master of your own conceptualizations. You must develop the ability to mentally "remove" this or that concept from the things named by the concept, and try out alternative ideas. As general semanticists often say: "The word is not the thing! The word is not the thing!" If you are trapped in one set of concepts (ideas, words), you can think of things in only one way. Word and thing become one and the same in your mind.
To figure out the proper use of words, the proper way to conceptualize things, events, situations, emotions, abstract ideas, it is important to first achieve a true command of the uses of words. For example, if you are proficient in the use of the English language, you recognize a significant difference in the language between needing and wanting, between having judgment and being judgmental, between having information and gaining knowledge, between being humble and being servile, between stubbornness and having the courage of your convictions. Command of distinctions such as these, and many others, in the language has a significant influence upon the way you interpret your experience. People who do not have this command confuse these important discriminations and distort the important realities they help us distinguish.
To the extent that you have a sound command of the English language, you should be able to state the essential differences between related but distinguishably different realities that are marked by words or expressions in our language. To the extent that you can, you are conceptualizing the ideas labeled with these words in keeping with educated use. In this activity, you will test your ability to do this. What follows is a set of related words, each pair illustrating an important distinction marked by our language. For each set, write down your understanding of the essential difference between each word pair. After you have done this for each set of words, look up the words in the dictionary, and see how close your ideas of the essential difference of the word pair were to the actual distinctions the dictionary entries state or imply. (We recommend the Webster's New World Dictionary.) |
In learning to speak our native language, we learn thousands of concepts. When properly used, these concepts enable us to make legitimate inferences about the objects of our experience. Unfortunately, nothing in the way we ordinarily learn to speak a language forces us to use concepts carefully or prevents us from making unjustifiable inferences in using them.
Often we misuse or confuse ideas because of our indoctrination into a social system, resulting in a distortion of our experience. As developing thinkers, we must continually distinguish the concepts and ideas implicit in our social conditioning from the concepts and ideas implicit in the natural language we speak. For example, people from many different countries and cultures speak the same natural language. The peoples of Canada, Ireland, Scotland, England, Australia, Canada, and the United States all speak English. By and large, they implicitly share (to the extent to which they are proficient in the language) the same set of concepts (codified in the 23 volumes of the Oxford English Dictionary). Nevertheless, the people in these countries are not socially conditioned in the same way.
What is more, a person from China or Tibet could learn to speak the English language fluently without in any sense sharing in the same social conditioning. Because of this, natural languages (French, German, English, Swahili, or Hindi are examples) are repositories of concepts that, by and large, are not to be equated with the concepts implicit in the social indoctrination of any social or cultural group speaking the language. This is a difficult insight to gain, but it is a powerful and essential one.
In the United States, for example, most people are socially conditioned to believe that capitalism is superior to any other economic system (it is called "free enterprise"). Americans assume that no country can be truly democratic unless it has a capitalistic economic system. Furthermore, Americans assume that the major opposing systems, socialism or communism, are either wrong, enslaving, or evil (the "Evil Empire"). People in the U.S. are encouraged to think of the world in these ways by movies, the news, schooling, political speeches, and many other social rituals. Raised in the United States, Americans internalize different concepts, beliefs, and assumptions about themselves and the world than they would have had they grown up in China or Iran, for example.
Nevertheless, in a decent dictionary of the English language, lexicographers would not confuse these socially implied meanings and psychological associations with the foundational meanings of the words. The term communism would not be defined as "an economic system that enslaves the people." The word capitalism would not have the definition, "an economic system essential to a democratic society."
Nevertheless, because we are socialized to believe that we, as a people, are free, reasonable, just, and caring, we assume that our behavior matches what these words imply. Words often substitute, in human life, for the realities named by them. Fundamental contradictions or inconsistencies in our lives, then, go unquestioned. This is part of the self-deceptive tendencies to which the human mind is prone.
Critical thinkers learn how to strip off surface language and consider alternative ways to talk and think about things. For example, when thinking sociocentrically, we become trapped in the view of our peer group and society with little or no conscious awareness of what it would be to rationally decide upon alternative ways to conceptualize situations, persons, and events. Most people are awed by social ritual, in particular the trappings of social authority, status, and prestige. They live their life, as it were, in surface structures. Critical thinkers learn how to think sociologically. They therefore come to recognize when their ideas are controlled by social rituals, social expectations, and taboos.
Thinking with Information
It is impossible to reason without using some set of facts, data, or experiences as a constituent part of one's thinking. Finding trustworthy sources of information and refining one's own experience critically are important goals of critical thinkers. We must be vigilant about the sources of information we use. We must be analytically critical of the use we make of our own experience. Experience may be the best teacher, but biased experience supports bias, distorted experience supports distortion, and self-deluded experience supports self-delusion. We, therefore, must not think of our experience as sacred in any way but, instead, as one important dimension of thought that must, like all others, be critically analyzed and assessed.
Numerous problems exist in human life because people fail to understand the important role that information plays in everything we do. People often, for example, fail to see that they are excluding important information from their thinking when reasoning through a complex problem. People often operate on automatic pilot when it comes to their use of information. But when they are explicitly aware of the importance of information, they are much more careful in the conclusions they come to. They seek information when others would ignore the need to do so. They question the information they have, as well as the information that others are using. They realize that their thinking can only be as good as the information they use to come to conclusions.
Distinguishing Between Inert Information, Activated Ignorance, and Activated Knowledge
The mind can take in information in three distinctive ways: 1) by internalizing inert information; 2) by forming activated ignorance; and 3) by achieving activated knowledge.
Inert Information
By inert information, we mean taking into the mind information that, though memorized, we do not understand - despite the fact that we think we do. For example, many people have taken in, during their schooling, a lot of information about democracy that leads them to believe they understand the concept. Often, a good part of the information they have internalized consists of empty verbal rituals. For example, many children learn in school that "democracy is government of the people, by the people, for the people." This catchy phrase often sticks in their mind. It leads them to think they understand what it means, though most of them do not translate it into any practical criteria for assessing the extent to which democracy does or does not exist in any given country. Most people, to be explicit, could not intelligibly answer any of the following questions:
- What is the difference between a government of the people and a government for the people?
- What is the difference between a government for the people and a government by the people?
- What is the difference between a government by the people and a government of the people?
- What exactly is meant by "the people?"
Thus, people often do not sufficiently think about information they memorized in school to transform it into something truly meaningful in their mind. Much human information is, in the mind of the humans who possess it, merely empty words (inert or dead in the mind). Critical thinkers try to clear the mind of inert information by recognizing it as such and transforming it, through analysis, into something meaningful.
Review information you were taught in school or at home. Look for what you may have repeated often on command, to see if it qualifies for what we are calling inert information. Review, for example, the Pledge of Allegiance to the flag, slogans within subject fields, memorized bits and pieces of content, and sayings you have often heard, but probably have not made sense of. See how many candidates you can locate for inert information. Test each one with this criterion: If you cannot explain it or effectively use it, it is likely to be inert information in your mind. If, by chance, you do not find this sort of information, don't assume that you are free of inert information. |
Activated Ignorance
By activated ignorance, we mean taking into the mind, and actively using, information that is false, though we mistakenly think it to be true. The philosopher Rene Descartes came to confidently believe that animals have no actual feelings, but are simply robotic machines. Based on this activated ignorance, he performed painful experiments on animals and interpreted their cries of pain as mere noises.
Some people believe, through activated ignorance, that they understand things, events, people, and situations that they do not. They act upon their false ideas, illusions, and misconceptions, often leading to needless waste, pain, and suffering. Sometimes activated ignorance is the basis for massive actions involving millions of people (think of the consequences of the Nazi idea that Germans were the master race and Jews an inferior race). Sometimes it is an individual misconception that is acted on only by one person in a limited number of settings. Wherever activated ignorance exists, it is dangerous.
It is essential, therefore, that we question our beliefs, especially when acting upon them has significant potential implications for the harm, injury, or suffering of others. It is reasonable to suppose that everyone has some beliefs that are, in fact, a form of activated ignorance. Eliminating as many such beliefs as we can is a responsibility we all have. Consider automobile drivers who are confident they can drive safely while they are intoxicated. Consider the belief that smoking does not have any significant negative health effects.
It is not always easy to identify what is and is not activated ignorance. The concept of activated ignorance is important regardless of whether we determine information we come across is false or misleading. What we need to keep in mind are clear-cut cases of activated ignorance so we have a clear idea of it, and personal vigilance with respect to the information we come across that is potentially false. Most people who have acted harmfully as a result of their activated ignorance have probably not realized that they hurt others. Ignorance treated as the truth is no trivial matter.
Review what you were taught in school, college, at work, or at home. Seek what you used to believe to be true but now have found to be false and harmful. For example, you probably picked up some activated ignorance from your peer group as you were growing up. Think of things you learned "the hard way." See how many candidates you can locate for activated ignorance. Test each one with this criterion: At one time I thought this was true. Now I know it is false. If, by chance, you do not find any, don't assume that you are free of activated ignorance. Pursue why you are having trouble finding it. |
Activated Knowledge
By activated knowledge, we mean taking into the mind, and actively using, information that is not only true but that, when insightfully understood, leads us by implication to more and more knowledge.
Scientists have activated knowledge of the scientific method. They use this method (of hypothesis, prediction, controlled experiment, observation, and provisional conclusions) to acquire more and more knowledge. The method is powerful, enforces discipline on human thinking, and provides safeguards against misuse.
The basic principles of mathematics represented activated knowledge about numbers, shapes, space, and motion that enable the careful thinker to develop precise conclusions based on precise information.
The basic principles of critical thinking represent activated knowledge of the parts of thinking, standards by which thinking can be assessed, and ways in which thinking can be improved. These principles can be applied again and again with the consequence that we discover further knowledge on the basis of our present knowledge and disciplined thought about new information.
Some Key Questions to Ask When Pursuing Information
One of the most important skills in critical thinking is that of evaluating information. This skill begins with the important recognition that information and fact, information and verification, are not the same thing. It requires also the important recognition that everything presented as fact or as true is not. A third important recognition is that the prestige or setting in which information is asserted, as well as the prestige of the person or group asserting it, are no guarantee of accuracy or reliability. Consider the following, very helpful, maxim: An educated person is one who has learned that information almost always turns out to be at best incomplete and very often false, misleading, fictitious, and mendacious - that is, information is often just dead wrong.
Careful professionals use a wide variety of safeguards in the disciplines in which they work. It is not possible to learn these safeguards separately from an actual study of the disciplines. However, it is possible to develop a healthy skepticism about information in general, especially about information presented in support of a belief that serves the vested interests of a person or group. This skepticism is given in the regular asking of key questions about information presented to us:
To what extent could I test the truth of this claim by direct experience?
To what extent is believing this consistent with what I know to be true or have justified confidence in?
How does the person who advances this claim support it?
Is there a definite system or procedure for assessing claims of this sort?
Does the acceptance of this information advance the vested interest of the person or group asserting it?
Is the person asserting this information made uncomfortable by having it questioned?
These questions, both singly and as a group, are no panacea. Everything depends on how we follow up on them. Used with good judgment, they help us to lower the number of mistakes we make in assessing information. They do not prevent us from making such mistakes. In later chapters, we will follow up on these concerns in a deeper way. You should begin now, however, to practice asking the above questions when information is presented to you as true and important.
Assess the following claims by figuring out whether you think they are true or false. Explain your reasoning: |
Distinguishing Between Inferences and Assumptions
As we have said, the elements of reasoning interrelate. They are continually influencing and being influenced by one another. We now will focus at length on the crucial relationship between two of the elements: inference and assumption. Learning to distinguish inferences from assumptions is an important skill in critical thinking. Many confuse the two elements. Let us begin with a review of the basic meanings:
- Inference: An inference is a step of the mind, an intellectual act by which one concludes that something is true in light of something else's being true, or seeming to be true. If you come at me with a knife in your hand, I probably would infer that you mean to do me harm. Inferences can be accurate or inaccurate, logical or illogical, justified or unjustified.
- Assumption: An assumption is something we take for granted or presuppose. Usually it is something we previously learned and do not question. It is part of our system of beliefs. We assume our beliefs to be true and use them to interpret the world about us. If you believe that it is dangerous to walk late at night in big cities and you are staying in Chicago, you will infer that it is dangerous to go for a walk late at night. You take for granted your belief that it is dangerous to walk late at night in big cities. If your belief is a sound one, your assumption is sound. If your belief is not sound, your assumption is not sound. Beliefs, and hence assumptions, can be unjustified or justified, depending upon whether we do or do not have good reasons for them. Consider this example: "I heard a scratch at the door. I got up to let the cat in." My inference was based on the assumption (my prior belief) that only the cat makes that noise, and that she makes it only when she wants to be let in.
We humans naturally and regularly use our beliefs as assumptions and make inferences based on those assumptions. We must do so to make sense of where we are, what we are about, and what is happening. Assumptions and inferences permeate our lives precisely because we cannot act without them. We make judgments, form interpretations, and come to conclusions based on the beliefs we have formed (see Figure 6.5 ).
Figure 6.5. Humans routinely draw conclusions in situations. Those conclusions are based on assumptions that usually operate at an unconscious level.
If you put humans in any situation, they start to give it some meaning or other. People automatically make inferences to gain a basis for understanding and action. So quickly and automatically do we make inferences that we do not, without training, notice them as such. We see dark clouds and infer rain. We hear the door slam and infer that someone has arrived. We see a frowning face and infer that the person is angry. If our friend is late, we infer that she is being inconsiderate. We meet a tall guy and infer that he is good at basketball, an Asian and infer that she will be good at math. We meet a well-dressed person and infer he or she is successful. We think of the business we would like to start and infer it will be successful - because we ourselves desire what it will sell.
As we write, we make inferences as to what readers will make of what we are writing. We make inferences as to the clarity of what we are saying, what requires further explanation, what has to be exemplified or illustrated, and what does not. Many of our inferences are justified and reasonable, but some are not.
As always, an important part of critical thinking is the art of bringing what is subconscious in our thought to the level of conscious realization. This includes the skill of identifying and reconstructing the inferences we make so the various ways in which we shape our experiences through our inferences become more and more apparent to us. This skill enables us to separate our experiences into two categories. We learn to distinguish the raw data of our experience from our interpretations of those data, from the inferences we are making about them. Eventually we need to realize that the inferences we make are heavily influenced by our point of view and the assumptions we have made about people and situations. This puts us in the position of being able to broaden the scope of our outlook, to see situations from more than one point of view, and hence to become more open-minded.
Often different people make different inferences because they bring to situations different points of view. They see the data differently. To put it another way, they have different assumptions about what they see. For example, if two people see a man lying in a gutter, one might infer, "There's a drunken bum." The other might infer, "There's a man in need of help." These inferences are based on different assumptions about the conditions under which people end up in gutters, and these assumptions are connected to the point of view about people that each has formed. The first person assumes, "Only drunks are to be found in gutters." The second person assumes, "People lying in the gutter are in need of help."
The first person may have developed the point of view that people are fundamentally responsible for what happens to them and ought to be able to take care of themselves. The second may have developed the point of view that the problems people have are often caused by forces and events beyond their control. The reasoning of these two people, in terms of their inferences and assumptions, could be characterized in the following way:
Situation: A man is lying in the gutter. | Situation: A man is lying in the gutter. |
Inference: That man's a bum. | Inference: That man is in need of help. |
Assumption: Only bums lie in gutters. | Assumption: Anyone lying in the gutter is in need of help. |
As persons concerned with developing our thinking, we want to begin to notice the inferences we are making, the assumptions we are basing those inferences on, and the point of view about the world we are developing. To do this, we need lots of practice in noticing our inferences and then figuring the assumptions that lead to them.
As thinkers, it is important that we be able to distinguish among information, inferences, and assumptions. Whenever we are in a situation, we naturally make inferences. We come to conclusions about the situation or give it meaning through our interpretations. And these inferences result from the assumptions we made or are making. For example: Then: In the following activity, we will provide you with situations (information). We want you to figure out what someone might infer (rightly or wrongly) in the situation. Usually there is a range of possible inferences that different people might make, depending on their various beliefs. Then, having stated what you think someone might infer, figure out the assumption that would lead someone to make that inference. As a suggestion, first figure out a likely inference (whether rational or irrational), then, and only then, try to figure out the assumption. The assumption will be a generalization that led the person to make the inference. We have provided two examples to help you begin.
Our goal of becoming aware of the inferences we make and the assumptions that underlie our thinking enables us to begin to gain command over our thinking. Because all human thinking is inferential in nature, command of our thinking depends on command of the inferences embedded in it and thus of the assumptions that underlie it. Consider the way in which we plan and think our way through everyday events. We think of ourselves as preparing for breakfast, eating our breakfast, getting ready for work, arriving on time, attending meetings, completing necessary tasks, making plans for lunch, paying bills, engaging in small talk, and so on. Another way to put this is to say that we are continually interpreting our actions, giving them meanings, and making inferences about what is going on in our lives. That is, we must choose among a variety of possible meanings. For example, am I "relaxing" or "wasting time"? Am I being "determined" or "stubborn"? Am I "joining" a conversation or "butting in"? Is someone "laughing with me" or "laughing at me"? Am I "helping a friend" or "being taken advantage of?" Every time we interpret our actions, every time we give them a meaning, we are making one or more inferences on the basis of one or more assumptions. As humans, we continually make assumptions about ourselves, our jobs, our mates, our teachers, our parents, and the world in general. We take some things for granted simply because we can't question everything. Sometimes we take the wrong things for granted. For example, I run off to the store (assuming that I have enough money with me) and arrive to find that I have left my money at home. I assume that I have enough gas in the car only to find that I have run out of gas. I assume that an item marked down in price is a good buy only to find that it was marked up before it was marked down. I assume that it will not, or that it will, rain. I assume that my car will start when I turn the key and press the gas pedal. I assume that I mean well in my dealings with others. We make hundreds of assumptions without knowing it - without thinking about it. Most of them are sound and justifiable. Some, however, are not. The question then becomes: "How can we begin to recognize the inferences we are making, the assumptions we are basing those inferences on, and the point of view, the perspective on the world that we are forming?" As we become skilled in identifying our inferences and assumptions, we are in a good position to question the extent to which any one of our assumptions is justified. For example, are we justified in assuming that everyone eats lunch at 12:00 noon? Are we justified in assuming that it usually rains when there are black clouds in the sky? Are we justified in assuming that black eyes are only caused by someone hitting another person? The point is that we all make many assumptions as we go about our daily life and we ought to be able to recognize and question them. As you develop these critical intuitions, you should increasingly notice your inferences and those of others. You should increasingly notice what you and others are taking for granted. You should increasingly notice how your point of view shapes your experiences.
|
IMAGES
VIDEO
COMMENTS
Module 7: Thinking, Reasoning, and Problem-Solving. This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure ...
Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue. The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything ...
How is problem solving related to other forms of high-level cognition processing, such as thinking and reasoning? Thinking refers to cognitive processing in individuals but includes both directed thinking (which corresponds to the definition of problem solving) and undirected thinking such as daydreaming (which does not correspond to the definition of problem solving).
Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries. Today, it is one area of psychological study. ... While language is a form of communication, not all communication is language. ... Problem-solving abilities can improve with practice. Many people challenge themselves every day ...
Additional Problem Solving Strategies:. Abstraction - refers to solving the problem within a model of the situation before applying it to reality.; Analogy - is using a solution that solves a similar problem.; Brainstorming - refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal ...
Thinking and Language. Figure 1. Thinking is an important part of our human experience, and one that has captivated people for centuries. Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the 20th-century sculpture The Thinker by August Rodin, and Shi Ke's 10th-century painting ...
6. Thinking and Intelligence. Figure 7.1 Thinking is an important part of our human experience, and one that has captivated people for centuries. Today, it is one area of psychological study. The 19th-century Girl with a Book by José Ferraz de Almeida Júnior, the 20th-century sculpture The Thinker by August Rodin, and Shi Ke's 10th-century ...
Simply put, critical thinking is the act of deliberately analyzing information so that you can make better judgements and decisions. It involves using things like logic, reasoning, and creativity, to draw conclusions and generally understand things better. This may sound like a pretty broad definition, and that's because critical thinking is a ...
e. Problem solving is the process of achieving a goal by overcoming obstacles, a frequent part of most activities. Problems in need of solutions range from simple personal tasks (e.g. how to turn on an appliance) to complex issues in business and technical fields. The former is an example of simple problem solving (SPS) addressing one issue ...
Abstract. This chapter follows the historical development of research on problem solving. It begins with a description of two research traditions that addressed different aspects of the problem-solving process: (1) research on problem representation (the Gestalt legacy) that examined how people understand the problem at hand, and (2) research on search in a problem space (the legacy of Newell ...
As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity, intelligence testing, and how our biology and environments interact to affect intelligence. After finishing this module, you will have a greater appreciation of the higher-level ...
The problem-solving process typically includes the following steps: Identify the issue: Recognize the problem that needs to be solved. Analyze the situation: Examine the issue in depth, gather all relevant information, and consider any limitations or constraints that may be present. Generate potential solutions: Brainstorm a list of possible ...
Cognitive psychology seeks to understand all of the mental processes involved in human thought and behavior. It focuses on cognitive processes such as decision-making, problem-solving, attention, memory, learning, and more. Keep reading to learn more about different types of cognitive processes, factors that can affect cognition, and the ...
Creating involves putting elements together to form a coherent or functional whole. Creating includes reorganizing elements into a new pattern or structure through planning. This is the highest and most advanced level of Bloom's Taxonomy. Build a model and use it to teach the information to others.
Critical thinking involves asking questions, defining a problem, examining evidence, analyzing assumptions and biases, avoiding emotional reasoning, avoiding oversimplification, considering other interpretations, and tolerating ambiguity. Dealing with ambiguity is also seen by Strohm & Baukus (1995) as an essential part of critical thinking ...
These situations require problem solving. Generally, problem solving is the thinking that occurs if we want "to overcome barriers between a given state and a desired goal state by means of ... The data used to support the findings cannot be shared at this time as it also forms part of an ongoing study. Conflicts of Interest. Authors declare ...
Get unlimited access to all our career-boosting content and member benefits with our 7-day free trial. Although problem-solving is something everyone does on a daily basis, many people lack confidence in their ability. Here we look at the basic problem-solving process to help keep you on the right track.
Once we have done so, we apply the standards for thinking to those parts (standards such as clarity, accuracy, relevance, logicalness, fairness, etc.). Once we have a clear understanding of the parts of thinking (or elements of reasoning) and the intellectual standards, and once we begin to use them in our thinking on a daily basis, we begin to ...
ADVERTISEMENTS: In this article we will discuss about:- 1. Introduction to Thinking 2. Types of Thinking 3. Development 4. Tools 5. Errors. Introduction to Thinking: Cognitive abilities like thinking, reasoning and problem-solving may be considered to be some of the chief characteristics which distinguish human beings from other species including the higher animals. The challenges […]
Convergent Thinking. Convergent thinking patterns are problem solving techniques that unite different ideas or fields to find a solution. The focus of this mindset is speed, logic and accuracy, also identification of facts, reapplying existing techniques, gathering information.
Critical thinking and analysis. Complex problem-solving. Leadership and social influence. Emotional intelligence. Reasoning, problem-solving and ideation. Systems analysis and evaluation. It's interesting that at least 7 of the top 10 hinge on one or more forms of thinking mentioned above.
Problem-solving, particularly creative problem solving, incorporates critical thinking with creativity to devise effective and innovative solutions. To excel in this area, you must be adept at both analyzing and synthesizing information. Analysis: Break down complex issues into manageable parts. Synthesis: Combine elements in novel ways to form ...
Problem solving forms part of thinking. Note: The above text is excerpted from the Wikipedia article "Problem solving", which has been released under the GNU Free Documentation License. For more ...
Logical thinking is an excellent way of solving complex problems. You can break the problem into smaller parts; solve them individually in a sequence, then present the complete solution. However, it is not infallible. So, when a problem in the workplace feels overwhelming, you may want to think about it logically first.
Problem-solving strategies can be enhanced with the application of creative techniques. You can use creativity to: Approach problems from different angles. Improve your problem-solving process. Spark creativity in your employees and peers. 6. Adaptability. Adaptability is the capacity to adjust to change. When a particular solution to an issue ...
Learning to recognize one's distortions in thinking that are creating problems, and then to reevaluate them in light of reality. Gaining a better understanding of the behavior and motivation of others. Using problem-solving skills to cope with difficult situations. Learning to develop a greater sense of confidence in one's own abilities.
These include problems with memory, thinking, problem-solving or language, and often changes in emotions, perception or behaviour. As dementia progresses, a person will need more help and, at some point, will need a lot of support with daily living. However, dementia is different for everyone, so it will vary how soon this happens and the type ...
Machine learning is a form of artificial intelligence that can adapt to a wide range of inputs, including large sets of historical data, synthesized data, or human inputs. (Some machine learning algorithms are specialized in training themselves to detect patterns; this is called deep learning. See Exhibit 1.)