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• Data Descriptor
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• Published: 20 January 2022

Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals

• Maciej Behnke   ORCID: orcid.org/0000-0002-2455-4556 1 , 2 ,
• Mikołaj Buchwald 3 ,
• Adam Bykowski 3 ,
• Szymon Kupiński   ORCID: orcid.org/0000-0002-4704-6802 3 &
• Lukasz D. Kaczmarek 1  

Scientific Data volume  9 , Article number:  10 ( 2022 ) Cite this article

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• Cardiovascular biology

Subjective experience and physiological activity are fundamental components of emotion. There is an increasing interest in the link between experiential and physiological processes across different disciplines, e.g., psychology, economics, or computer science. However, the findings largely rely on sample sizes that have been modest at best (limiting the statistical power) and capture only some concurrent biosignals. We present a novel publicly available dataset of psychophysiological responses to positive and negative emotions that offers some improvement over other databases. This database involves recordings of 1157 cases from healthy individuals (895 individuals participated in a single session and 122 individuals in several sessions), collected across seven studies, a continuous record of self-reported affect along with several biosignals (electrocardiogram, impedance cardiogram, electrodermal activity, hemodynamic measures, e.g., blood pressure, respiration trace, and skin temperature). We experimentally elicited a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat. Psychophysiology of positive and negative emotions (POPANE) database is a large and comprehensive psychophysiological dataset on elicited emotions.

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17061512

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Background & summary.

The emotional response involves changes in subjective experience and physiology that mobilize individuals towards a behavioral response 1 , 2 , 3 , 4 , 5 , 6 . Theorists have debated for decades on the psychophysiology of human emotions focusing on several questions 7 , 8 , 9 , 10 . For instance, whether specific emotions produce a specific physiological response 3 , how different biosignals are correlated within an emotional response 5 , 6 , whether the physiological response allows predicting concurrent subjective experience 11 , what new features within a specific biosignal (e.g., the ECG wave) are influenced by emotions 12 , what improved methods of data processing can be used 13 , how emotions influence physiological patterns related to health 14 , 15 .

Physiological responses to the emotional stimuli were primarily of interest in psychology. However, emotions have recently also gained attention in other scientific fields, such as neuroscience 16 , product and experience design 17 , and computer science 18 . For instance, Affective Computing (an interdisciplinary field also known as Emotional AI) uses psychophysiological signals for developing algorithms that allow detecting, processing, and adapting to others’ emotions 19 , 20 . To allow machines to learn about specific emotion features, researchers have to provide these machines with multiple descriptors of emotional response, including subjective experience of affect (e.g., valence and motivational tendency) and objective physiological measures (e.g., cardiovascular, electrodermal, and respiratory measures).

These basic science and applied problems require robust empirical material that provides a large and comprehensive dataset that offers abundant emotions, diverse physiological signals, and the number of participants providing high statistical power. Moreover, researchers use various methods to elicit emotions 21 , including film clips 22 , 23 , pictures 24 , video recording/social pressure 25 , 26 , and behavioral manipulations 27 . Thus, accounting for various methods of emotion elicitation might contribute to database versatility.

A considerable amount of work has been done during the last two decades for creating multimodal datasets with psychophysiological responses to affective stimuli, including DEAP 28 , RECOLA 29 , CASE 30 , or K-EmoCon 31 . The strengths of our database – Psychophysiology of Positive and Negative Emotions (POPANE) 32 are:

a wide range of positive and negative emotions, including amusement, anger, disgust, excitement, fear, gratitude, sadness, tenderness, and threat;

multiple methods to elicit emotions, namely: films, pictures, and affective social interactions (anticipated social exposition or expressing gratitude)

continuous emotional responses via self-reports and autonomic nervous system (ANS) activity using electrocardiography, impedance cardiography, electrodermal activity sensors, photoplethysmography (the hemodynamic measures), respiratory sensors, and a thermometer;

the length of our data is up to 725 hours of recordings, depending on the signal type. Table  1 presents the signal length for specific measures, stimuli, and emotion categories.

POPANE contains psychophysiological data from seven large-scale experiments that investigated the functions of positive and negative emotions. The studies tested how emotions influence the speed of cardiovascular recovery (Study 1 & 2) 33 , motivation to engage in a positive psychological intervention (Study 3) 34 , economic decisions (Study 4) 35 , responses to others successes (Study 5) 36 , responses to an unfair offer (Study 6) 37 , and gaming efficacy (Study 7) 38 , 39 . Tables  1 and 2 summarize the POPANE dataset. Figure  1 presents a schematic overview of the experimental setup used to collect the data.

A schematic visualization of the data acquisition procedure. Panel a presents the approximate placement of the sensors. Panel b presents hardware (in white) and software (in grey) used for data acquisition. This figure was created by Katarzyna Janicka. The copyright of the figure is held by Katarzyna Janicka.

We present data collected in the Psychophysiological Laboratory, at the Faculty of Psychology and Cognitive Science, Adam Mickiewicz University, from November 2016 to July 2019 in Poznan, Poland. All methods are described in detail in the following works: Study 1 & 2 33 , Study 3 34 , Study 4 35 , Study 5 36 , Study 6 37 , Study 7 38 .

Participants

The database includes 1157 cases (45% female) between the ages of 18 and 38 ( M  = 22.01, SD  = 2.80). Table  2 presents participants’ characteristics for each study. We recruited participants via advertisements on Facebook and internal University communication channels. We asked participants to reschedule if they felt sick or experienced a serious negative life event and to abstain from vigorous exercise, food, and caffeine for two hours before testing. In recruitment, we invited participants that: 1) were healthy – had no significant health problems, 2) did not use drugs nor medications that might affect cardiovascular functions, 3) had no prior diagnosis of cardiovascular disease or hypertension. We introduced the above exclusion criteria to limit factors that might influence cardiovascular functions. We measured the participants’ height using an anthropometer and weight using an Omron BF511 scale (Omron Europe B.V., Netherlands). Each participant provided written informed consent and received vouchers for a cinema ticket for participation in the study. Of the participants, 895 participated in a single study, 101 in two studies, 19 in three studies, and two in four studies. The participants’ numerical IDs are presented in a metadata file. Next to specific within-study ID, we present the participants’ IDs from other studies so that within-person analyses might be possible to perform.

Ethics statement

All studies were approved by and performed in accordance with guidelines and regulations of the Institutional Ethics Committee at the Faculty of Psychology and Cognitive Science, Adam Mickiewicz University.

Procedures common across the studies

In most of our studies, participants were tested individually in a sound-attenuated and air-conditioned room. Study 5 involved opposite-sex couples tested together in the same room but in separate cubicles, with no interaction with each other. Participants were randomly assigned to the experimental conditions. We also randomized the order of affective stimuli within the studies. Detailed information on the order of affective stimuli in each study is available in the metadata file. All instructions were presented, and responses were collected via a PC with a 23-inch screen. The experiments were run in the e-Prime 2.0 (Study 1, 2, 3, 4) and 3.0 (Study 5, 6, 7) Professional Edition environment (Psychology Software Tools).

Upon arrival in the lab, participants provided informed consent, and the researcher applied sensors to obtain psychophysiological measurements. Studies began with a five-minute resting baseline (only Study 3 began with a three-minute baseline). During baseline, participants were asked to sit and remain still. Upon completing all studies, biosensors were removed, and the participants were debriefed.

After the baseline, participants completed the speech preparation task, which aimed at threat elicitation. Later, depending on randomization, affective pictures were presented on the PC screen to elicit high-approach motivation positive affect, low-approach motivation positive affect, or the neutral state for three minutes.

After the baseline, participants watched affective pictures (high-approach positive affect, low-approach positive affect, or neutral depending on randomization) for three minutes. Afterward, they were asked to prepare the speech which aimed at threat or anger elicitation (depending on randomization).

After completing the baseline, participants were requested to send two text messages: one expressing gratitude and one neutral. The order in which the messages were sent was counterbalanced. Before sending each SMS, participants were instructed to relax for three minutes and report their appraisals. Next, for another three minutes, participants were asked to think about a person to whom they were grateful for something. Afterward, participants were asked to send the message and wait for three minutes as the time needed for physiological recovery.

Participants were told that they would be participating in two unrelated studies. The purpose of the first study was presented as determining the relationship between language orientation and the psychophysiological reactions to film clips. The purpose of the second study was presented as evaluating consumer products. After baseline, participants solved linguistic tasks. Next, they watched fear or neutral state eliciting film clips (depending on randomization). After the emotion manipulation, participants reported social needs and evaluated six pairs of commercial products.

After completing the baseline, each participant was told to wait for their partner who would solve complex tasks. In fact, there were no tasks to be actually solved by any of the participants. Next, each participant completed three rounds consisting of 1) two minutes of watching the film clips while waiting for the partner; 2) receiving bogus information about the partners’ success; and 3) sending the feedback. Participants watched one of the three film sets, including only positive emotions, negative emotions, or a neutral condition (depending on randomization). The film clips were presented in a counterbalanced order.

After the baseline, participants watched one of four 12-minute films’ presentations eliciting only positive emotions, only negative emotions, the mix of positive and negative emotions, and neutral states (depending on randomization). After watching the set of films, participants were instructed to play an ultimatum social game 40 . Participants received the offer, which was considered unfair by most people taking part in this type of research (“6 USD for me and 0.80 USD for you”). Next, participants were asked to decide to accept or reject the offer. Before receiving the offer and after deciding to accept or reject the offer, there was a 2-minute waiting period for recording physiological processes.

After the baseline, participants completed five rounds consisting of (1) a 2-minute resting period; (2) 2-minute emotion elicitation (watching a film clip); (3) self-reports; and (4) playing a FIFA 19 match.

Affective stimuli

Study 1 and 2 used validated pictures 33 from the Nencki Affective Pictures System 24 . We chose three sets of pictures to elicit: high-approach positive affect (Faces340; Landscapes008, L023, L100, L110, L117, L,140, L149, Objects078, O081, O096, O183, O254, O291, O323, People108, P173, P189), low-approach positive affect (Animals099, A153, Faces076, F113, F179, F228, F232, F234, F238, F330, F332, F337, F344, F347, F353, F358, Objects192, O260), and neutral experience (Faces157, F166, F167, F309, F312, Landscapes012, L016, L024, L056, L061, L067, L076, L079, Objects112, O204, O210, O310, O314). Study 1 & 2 used the same set of pictures.

Speech preparation

In Study 1, we elicited threat with a well-validated social threat protocol 25 , 26 , 41 . Participants were asked to prepare a 2-minute speech on the topic “Why are you a good friend?”. We informed participants that the speech would be recorded. Furthermore, participants received the information that they would be randomly selected to deliver the speech or not after the 30 s of speech preparation. However, after 30 s of preparations (anticipatory stress), each participant was informed that they were selected not to deliver the speech.

In Study 2, we randomly assigned participants to prepare a threat or anger-related speech. We used a similar method to elicit a threat as in Study 1, but participants were given 3 min to prepare the speech. Study 2 also intended to elicit anger with a similar method, i.e., anger recall task 42 , 43 , 44 . Participants were asked to prepare a speech on the topic “What makes you angry?”. Participants had 3 minutes to prepare the speech. After 3 minutes of both threat- and anger-related speeches, we informed participants that they were selected not to deliver the speech.

Interpersonal communication

In Study 3, participants expressed their gratitude (a positive relational emotion) towards their benefactors via texting. This intervention was developed within the field of positive psychology 45 . Participants express their gratitude towards their acquaintance by sending a text message during the laboratory session (Gratitude Texting). This intervention involved the essential elements of gratitude expression, including identification and appreciation of a good event, recognition of the benefactors’ role in generating the positive outcome, and the act of communicating gratitude itself 46 . In the control condition, we asked participants to send a neutral text message to their acquaintance with no suggestion regarding the topic. The control condition accounts for psychophysiological responses associated with texting in general 47 . Participants prepared their messages for three minutes.

We used validated and reliable film clips selected from emotion-eliciting video clip databases 22 , 23 , 48 , 49 , 50 . Each clip lasted two minutes (except for films in Study 4 that, in sum, lasted for 3 minutes 41 seconds). Most of the film clips were short excerpts from commercially available films. Within the sessions, clips were presented in a counterbalanced order. Table  1 , along with the metadata spreadsheet, presents which films were used to elicit emotions in the studies. The names of the film descriptions used for emotion elicitation are also available in the metadata file (the “ stimuli ” spreadsheet).

We elicited positive emotions with the following film clips: 1) A Fish Called Wanda (Surprisingly, the homeowners get inside and discover Archie dancing while naked); 2) The Visitors (Visitors damage the letter carrier’s car); 3) When Harry Met Sally (Sally pretends to have an orgasm in a restaurant); 4) The Dead Poets Society (Students climb on their desks to show their solidarity with their professor); 5) Life Is Beautiful (In a second world war prisoner’s camp, a father and a boy talk to the mother through a loudspeaker); 6) Benny & Joone (Benny plays dumb in the café); and 7) Summer Olympic Games (Athletes performing successfully and showing their joyful reactions). We used films 1–3 in Study 5, films 1–6 in Study 6, and films 1 & 7 in Study 7.

We elicited negative emotions with the following film clips: (1) The Blair Witch Project (the characters die in an abandoned house); (2) A Tale of Two Sister s (the clip begins with suspense and ends with an intense explosion) (3) American History X (A neo-nazi kills Blackman’s by smashing his head on the curb); (4) Man Bites Dog ( A hitman pulls out a gun, yelling at an elderly woman); (5) In the Name of the Fathe r (Interrogation scene); (6) Seven (the police find a decomposing corpse); (7) Dangerous Minds (The teacher informs the class about the death of their classmate); and (8) The Champ (the boy cries after his father dies). We used films 1 & 2 in Study 4, films 1 & 3–7 in Study 6, films 3–5 in Study 5, and films 3 & 8 in Study 7.

For neutral conditions, we used the following film clips: (1) Blue 1 (A man organizes the drawers in his desk, or a woman walks down an alley); (2) The Lover (The character walks around town); (3) Blue 3 (The character passes a piece of aluminum foil through a car window); (4) The Last Emperor 1 (Conversation between the Emperor and his teacher); (5) Blue 2 (A woman rides up on an escalator, carrying a box); (6) The Last Emperor 2 (City life scenes); (7) Twin Peaks: Fire Walk with Me (the character sweeps the floor in the bar). We used films 2 & 5 in Study 4, films 1, 3 & 4 in Study 5, films 1 & 3–7 in Study 6, and film 5 in Study 7.

Sensors & instruments

We present sensors and instruments used in our studies with examples illustrating their possible research applications.

Participants reported the affective experience to the emotional stimuli continuously with an electronic rating scale 51 . We investigated two dimensions of affect: valence (Study 3, 5, & 6) and approach/avoidance motivational tendency (Study 1,2, & 7). Valence is the degree of feeling pleasure or displeasure in response to a stimulus (e.g., object, event, or a person). Individuals experience positive valence while facing favorable objects or situations (e.g., smiling people or amusing events), and negative valence while facing unfavorable objects or situations (e.g., sad individuals) 24 . The approach/avoidance motivational tendency is the urge to move toward or away from an object 52 . Individuals experience high-approach motivation while facing desirable or appetitive objects or situations (e.g., delicious food or sexually attractive individuals), and high-avoidance motivation while facing undesirable or aversive objects or situations (e.g., accidents or infected individuals). We focused on valence because it is the most fundamental and well-studied dimension of the affect, and we focused on the approach/avoidance motivational tendency that is a rather novel dimension considered in the literature that might advance understanding emotions’ functions 53 .

Participants reported valence on a scale from 1 ( extremely negative ) to 10 ( extremely positive ) or approach/avoidance motivational tendency on a scale from 1 ( extreme avoidance motivational tendency ) to 10 ( extreme approach motivational tendency ). Participants were asked to adjust the rating scale position as often as necessary so that it always reflected how they felt at a given moment. For valence, we asked the participants to move the tag to the right side of the scale when they felt more positive or pleasant and to move the tag to the left side of the scale when they felt more negative or unpleasant. For the approach/avoidance motivational tendency, we asked the participants to move the tag to the right side of the scale when they felt the motivation to go toward or engage with the stimulus and to move the tag to the left side of the scale when they felt the motivation to go away or disengage with the stimulus. Previous research indicated that rating scales are valid for reporting the intensity of valence and approach/avoidance motivation 24 , 51 , 54 .

The signal was sampled at a rate of 1 kHz by Powerlab 16/35 (ADInstruments). Furthermore, we provided a validated positive-negative (Study 3–6) or approach-avoidance (Study 1,2 & 7) graphical scale modeled after the self-assessment manikin above the numeric scale 38 , 55 .

Electrocardiography

We used two electrocardiographs (ECG), BioAmp with Powerlab 16/35 AD converter (ADInstruments, New Zealand) (Study 1,2,4 & 5) and Vrije Universiteit Ambulatory Monitoring System (VU-AMS, the Netherlands) (Study 3, 6 & 7). We used pre-gelled AgCl electrodes placed in a modified Lead II configuration. The signal was stored on a computer with other biosignals using a computer‐based data acquisition and analysis system (LabChart 8.1; ADInstruments or VU-AMS Data, Analysis & Management Software; VU-DAMS 3.0). The ECG signal was sampled at a frequency of 1 kHz. ECG signal allows the computation of numerous indexes with the most popular involving 1) heart rate, which reflects the autonomic arousal, associated with, e.g., dually innervated sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) activity, and is related to motivational intensity, action readiness, and engagement 56 , 57 , and 2) heart rate variability linked with stress, self-regulatory efforts, and recovery from stress 58 .

Impedance Cardiography

We recorded the impedance cardiography (ICG) signal continuously and noninvasively with the Vrije Universiteit Ambulatory Monitoring System (VU-AMS, the Netherlands) following psychophysiological guidelines 59 , 60 . We used pre-gelled AgCl electrodes placed in a four-spot electrode array for ICG 59 . The signal was stored on a computer with other biosignals using a computer‐based data acquisition and analysis system (VU-DAMS 3.0). The ICG signal was sampled at a frequency of 1 kHz. ICG provided three channels: baseline impedance (Z0), sensed impedance signal (dZ), and its derivative over time (dZ/dt). In addition to ECG signal, ICG signal allows the computation of indexes linked to the pace and blood volume of the heartbeats, including (1) pre-ejection period reflecting sympathetic cardiac efferent activity which is associated, e.g., with motivational intensity and engagement 56 , 57 ; (2) stroke volume which is linked with stress 61 ; and (3) cardiac output which is used, e.g., to discriminate between challenge vs. threat stress response 62 .

Hemodynamic measures

We recorded hemodynamic responses using two models of the Finometer: Finometer MIDI (Finapres Medical Systems, Netherlands) (Study 1, 2, 4 & 5) and Finometer NOVA (Finapres Medical Systems, Netherlands) (Study 3, 5 & 6). Finometers provided systolic blood pressure (SBP), diastolic blood pressure (DBP), cardiac output (CO), and total peripheral resistance (TPR). SBP, DBP, CO, TPR were recorded continuously beat-by-beat (only in Study 1, we recorded SBP and DBP as a raw signal). Finometers use the volume-clamp method first developed by Penaz 63 to measure finger arterial pressure waveforms with finger cuffs. The data were exported to the Powerlab 16/35 data acquisition system (ADInstruments, New Zealand) and LabChart 8.1 (ADInstruments, New Zealand) (Study 1, 3 & 4) or collected with BeatScope 2.0 (Finapres Medical Systems, Netherlands) 64 (Study 2, 5 & 6). SBP and DBP is used to assess, e.g., effort investment 54 or cardiovascular health risk 65 , whereas CO and TPR are used to differentiate between challenge vs. threat stress response 56 .

Electrodermal activity

We recorded the electrodermal activity (EDA) with the GSR Amp (ADInstruments) at 1 kHz. We used electrodes with adhesive collars and sticky tape attached to the medial phalanges of digits II and IV of the left hand. The electrodes had a contact area of 8 mm diameter and were filled with a TD‐246 sodium chloride skin conductance paste. The signal was stored on a computer with other biosignals using a computer‐based data acquisition and analysis system (LabChart 8.1; ADInstruments). Skin conductance reflects beta-adrenergic sympathetic activity, and some examples of its use comprise mental stress, cognitive load, and autonomic arousal 66 .

Respiration

In Study 1, we recorded respiratory action with a piezo-electric belt, Pneumotrace II (UFI, USA), sampled at 1 kHz. The belt was attached around the upper chest near the level of maximum amplitude for thoracic respiration. The signal was stored on a computer with other biosignals using a computer‐based data acquisition and analysis system (LabChart 8.1; ADInstruments). The respiratory action allows the computation of respiratory rate and depth associated, e.g., with mental stress 67 , arousal 68 , and increases in negative emotion, e.g., anger and fear 5 .

Fingertip skin temperature

In Study 1, we measured fingertip temperature with a temperature probe attached to a Thermistor Pod (ADInstruments, New Zealand). The thermometer was attached at the distal phalange of the left hand’s V finger, sampled at 1 kHz. The signal was stored on a computer with other biosignals using a computer‐based data acquisition and analysis system (LabChart 8.1; ADInstruments). Changes in digit temperature reflect sympathetically innervated peripheral vasoconstriction and vasodilation that decreases or increases the fingertip temperature due to lower or higher blood supply. For instance, the fingertip temperature decreases in response to stress 69 and increases in response to joy 70 . Fingertip temperature is usually lower than other body temperature measures, e.g., the axillary or oral temperature 71 . Moreover, fingertip skin temperature can be much lower for some participants due to individual differences in hand morphology as well as ambient temperature. For instance, thermoregulatory cold-induced vasodilation occurs when hands are exposed to cold weather in winter 72 .

Data acquisition

Figure  1 presents the experiments and the data acquisition setup. Stimuli were managed through E-Prime (Psychology Software Tools, Inc.). E-Prime software sent the markers to the data acquisition devices (LabChart and VU-AMS), by which we were able to synchronize and merge the recordings from different devices into a single data file. The rating scale, ECG, EDA, thermometer, and respiratory belt were directly connected to the Powerlab 16/35 and then to the acquisition personal computer (PC) over a USB port. The ECG and ICG were directly connected to the VU-AMS and then to the acquisition PC3 over a USB port. The blood pressure measures were collected via a finger cuff directly connected to the Finometers and then to the acquisition PC over a USB port. We synchronized LabChart and VU-AMS with Finometer data by manually adding the markers at the same time during data recording. Data were managed in the following manner: 1) Powerlab data was stored in LabChart 8.0; 2) VU-AMS data was stored in VU-DAMS; and 3) Finometer data were stored in BeatScope. The acquired data from each participant was exported with the timestamp provided by the acquisition PC and markers into the TXT data files.

Data preprocessing

Physiological data collected across seven studies were exported from the acquisition formats by the first author [MBe]. The participants’ number differs from the initial studies due to various issues such as device malfunction, signal artifacts, and missing data files. We presented data that had high signal quality. Thus, some participants’ data from some channels (devices) were excluded, resulting in an 8% decrease in the participants’ pool.

The exported TXT, CSV, and metadata files were preprocessed using Python 73 , 74 scientific libraries (e.g., pandas 1.1.5, numpy 1.19.2; see Code Availability, for detailed information) (Fig.  2a ). All signals were resampled to 1 kHz, using the previous neighbor interpolation method (Fig.  2b ). Signals from different devices were time-synchronized using synchronization markers generated during experiments. We marked the baselines and emotion elicitations within the files. Finally, data across studies were exported to a normalized form, consisting of a header, a predefined file structure, and a standardized subject naming convention.

Schematic presentation of data preprocessing. The data were first exported from the acquisition software (panel a) and then preprocessed and integrated into CSV files. The resulting CSV files can be easily loaded into most statistical software and packages, such as IBM’s SPSS or Python Pandas & SciPy modules (for visualization in Python’s Pandas module, see panel b).

Data Records

The POPANE dataset is publicly available at the Open Science Framework repository 32 .

We present auxiliary information about the experiments in the metadata spreadsheet. The metadata file includes participants’ ID, sex, age, height, weight, experimental conditions for each study, stimuli order within a session, and information about missing data, outliers, and artifacts (sheets “Study 1–7”). Furthermore, the metadata file provides information on individuals that participated more than once across the studies by showing all their study-related IDs. The description of labels used for tagging discrete emotions is also available in the metadata file (the “ stimuli ” spreadsheet).

Dataset structure

The data repository consists of seven ZIP-compressed directories (folders), one for each study, e.g., “Study1” directory was compressed to “Study1.zip” archive file, “Study2” was compressed as “Study2.zip”, etc. Each of these directories contains a set of CSV files with psychophysiological information for particular subjects. We used a consistent CSV file naming convention, i.e.: “S < study_id > _P < participant_id > _ < phase_name > .csv”, where “S” stands for study, “P” for participants, e.g.: S1_P10_Baseline.csv, or S6_P4_Amusement1.csv. The “ < study_id > ” & “ < particpant_id > ” are natural numbers identifying a study and a participant. The “ < phase_name > “ is the name of the phase of an experiment, e.g., “Baseline”, or “Amusement1”. The description of all experimental-phase labels is explained in the metadata spreadsheet. All psychophysiological signals recorded during the experiment for each individual are also available in a single CSV datafile named “S < study_id > _P < participant_id > _All.csv”. All the other files for a particular participant named in the following manner: “S < study_id > _P < participant_id > _ < phase_name > .csv” are files containing a subset of records (an excerpt) extracted from a basic “S < study_id > _P < participant_id > _All.csv” file. Thus, “S < study_id > _P < participant_id > _All.csv” files store either signals related to a particular experimental phase or signals gathered during time intervals, where no experimental conditions were present, i.e., signals that were not related to the affective manipulation.

Furthermore, we also included one additional component, i.e., “POPANE dataset”. This component contains a set of ZIP-compressed directories with a set of CSV files with psychophysiological information for particular participants, baselines, and emotions. We grouped the datafiles from all studies into a single folder sorted by emotions. This simplifies the usage of our dataset as the single set of emotion-related data from all 1157 cases.

A sample from Study 1 is available for preview and testing and can be obtained from the data repository as “Study1_sample.zip”. The compressed sample file size is 42 MB (208 MB uncompressed), as compared to 2.0 GB (9.3 uncompressed) of the complete dataset for Study 1. This provides potential users of the dataset with an opportunity to get the notion of the data without downloading the whole dataset. For the visualization of these sample data, see Fig.  2b .

Single file structure

Each of the CSV files in the dataset has a 11-line header, i.e., each file’s first eleven rows start with a hash sign (“#“). In the header, file metadata is available, including:

ID of the study as a variable “Study_name”, e.g., “Study_7”;

participant’s ID within the study as a variable “Subject_ID”, e.g., “119”;

participant’s age as a variable “Participant_Age”, e.g., “23”;

participant’s sex coded as man = 0, woman = 1, as a variable “Subject_Sex”;

participant’s height in centimeters as a variable “Participants_Height”, e.g. “178”;

participant’s weight in kilograms as a variable “Participants_Weight”, e.g. “74”;

channel/sensor name as a variable “Channel_Name”, e.g., “timestamp”, “affect”, “ECG”, “dzdt”, “dz”, “z0”, or “marker”;

category of the data in each column as a variable “Data_Category”, e.g., “timestamp”, “data”, and “marker”;

units of the measurement as a variable “Data_Unit”, e.g.: “second”, “millivolt”, or “ohm”;

sample rate of data collection as a variable “Data_Sample_rate”, e.g.: “1000 Hz”, or “beat to beat”;

name of the device (manufacturer) used for data collection as a variable “Data_Device”, e.g., “LabChart_8.19_(ADInstruments,_New Zealand)”, “Response_Meter_(ADInstruments,_New Zealand)”, or “ECG (Vrije_Universiteit_Ambulatory_Monitoring_System,_VU-AMS,_the Netherlands)”.

If no data are available for the participant’s age, sex, height, and weight, we inserted a value of “−1”.

Following the header, each CSV file contains 7–12 columns, depending on the study. For studies in which data were gathered from more channels, there are more columns in CSV files. Sensor names used in all studies are consistent across all CSV files (see the metadata file). The first column of the data table (except for the header) contains timestamps, as provided by a clock on the main data acquisition (logging) computer – the timestamp format is time in seconds. In the last column, there is a marker that identifies the specific phase of the experiment. The metadata file provides a full explanation of the stimulus IDs used to mark the specific phase of the experiment, e.g., “−1” indicates the experimental baseline, while “107” indicates the neutral film clip “The Lover”. The columns in between the timestamp and the marker contain the physiological data (see Table  3 for details).

We used different acquisition programs; therefore, the exported data had to be integrated into a common format. An automatic preprocessing procedure was implemented in Python scripts. We converted the raw acquired data (obtained with a proprietary acquisition software) into a consistent format and saved it in CSV files. Consequently, data from several sources were integrated to be easily imported into all common statistical software packages. We also prepared examples in IPython Jupyter Notebooks presenting how to load and visualize psychophysiological data from sample files for Study 1. Both the conversion scripts and the Notebooks can be obtained from our source code repository available at GitHub: https://github.com/psychosensing/popane-2021 .

Technical Validation

Qualitative validation.

The data quality was assured by following recommendations in affective science 3 . First, we used validated methods (e.g., protocols and stimuli) to elicit emotions in our experiments. We used stimuli in line with well-established methods in the affective science 21 . Second, the data were collected by experimenters that completed 30 h training in psychophysiological research provided by MBe and LDK. Third, prior to performing preprocessing, the first author (MBe) visually inspected all physiological signals. Before inclusion in the database, MBe manually double-checked all datasets for missing or corrupted data. Table  4 presents missing data for each stimulus and physiological signal. The histograms in Figure  3 show the distributions of the selected physiological signals during the resting baseline. Figure  3 also presents that collected signals had standard ranges. For instance, most participants presented a healthy SBP and DBP range during the resting baseline of the experiments 75 . This figure does not present raw recordings (e.g., ECG in mV) that require further processing (e.g., breathing rate based on peak analysis).

Data histograms of baseline psychophysiological levels. This figure presents the distribution of the mean psychophysiological levels for resting baseline but does not present raw recordings (e.g., ECG in mV) that require further processing (e.g., analysis to calculate HR or HRV).

Quantitative validation

We evaluated the quality of the signal with the Signal-to-Noise Ratio (SNR). In order to calculate SNR across the diverse physiological signals, we used an algorithm based on the autocorrelation function of the signal, using the second-order polynomial for fitting the autocorrelation function curve 76 . The script we used for calculating SNR is available in the project’s GitHub repository ( https://github.com/psychosensing/popane-2021 ). We calculated SNR for all baselines and emotion elicitations across seven studies (Table  5 ). The calculated SNR indicated the high quality of all collected signals 77 , SNR min  = 5.67 dB, with mean SNR ranging from 37.82 dB to 67.39 dB depending on physiological signal and study. We identified outliers above SNRs’ z -scores higher than 3.29 78 , resulting in 290 parts (1.09% of all calculated SNR values) identified as SNR outliers. Next, the first author (MBe) visually inspected all flagged data to determine whether it should be classified as artifacts, resulting in 257 SNR outlying data points being identified as artifacts (88% of the low SNR data; less than 0.96% of all calculated SNR values). Both outliers and artifacts are presented in the metadata file.

Previous studies

For each study represented in the dataset, we ran manipulation checks that contributed to the technical validation. We found that the stimuli produced expected affective and physiological responses in participants 33 , 34 , 35 , 36 , 37 , 38 . For instance, in Study 5, we found that individuals who watched the positive film clips reported more positive valence, whereas individuals who watched the negative film clips reported more negative valence, compared to individuals who watched the neutral film clips 36 . Furthermore, individuals in the positive and negative emotion conditions displayed greater physiological reactivity (e.g., SBP and DBP) than individuals in the neutral conditions 36 .

Usage Notes

The POPANE dataset is available at https://doi.org/10.17605/OSF.IO/94BPX . The data in the datasets are saved in CSV format. The dataset can be used to test hypotheses on positive and negative emotions, create psychophysiological models and/or standards, or as an example data for testing technical aspects of the analyses and/or validation of mathematical models. These data can be of interest for several scientific fields such as psychology, e.g., for investigating human emotions based on physiological and psychometric information, or computer science (machine learning) for implementing automatic emotion recognition, or clustering data related to particular emotions.

Limitations

There are some shortcomings of our dataset. First, some data are missing because recordings for some of the participants could not be reliably collected due to technical reasons. Second, this dataset cannot be employed to investigate psychophysiological differences between ethnicities, neither between the group ages, as more than 99% of the participants were Caucasian young adults. This is an important limitation because some studies indicated physiological differences in baseline levels and reactivity to some stressors depending on the participant’s age 79 , 80 and ethnicity 81 . Moreover, some studies in the dataset recruited only male participants. This is important to control if the whole dataset would be used for testing hypotheses regarding sex differences 82 . Third, our dataset does not include participants diagnosed with cardiovascular disease. However, we did not collect information about other health issues, e.g., psychiatric or neurological diagnosis.

Fourth, this dataset is a posteriori use of the previously acquired data in already published independent studies. However, some participants (12%) took part in more than one study. We identified these participants in the metadata file. Thus, if the whole dataset is used to test hypotheses, researchers should consider this issue. In contrast, some authors might be particularly interested in the use of repeated data collected from the same participants, e.g., to test intraperson stability or change.

Fifth, most of the film clips were short excerpts from commercially available films. Thus, some of our participants might have already been familiar with them.

Sixth, in our studies, we measured autonomic nervous system (ANS) reactivity to nine discrete emotions. This is not an exhaustive list of affective states related to ANS activity. Future studies may focus on emotions that are examined less often in psychophysiological studies, including pride, craving, love, or embarrassment 6 . Furthermore, the emotions elicited in our studies were not balanced in valence, as some studies were focused on the differences between neutral conditions and positive emotions (Study 3) or negative emotions (Study 4).

In summary, the POPANE database is a large and comprehensive psychophysiological dataset on emotions. We hope that POPANE will provide individuals, companies, and laboratories with the data they need to perform their analyses to advance the fields of affective science, physiology, and psychophysiology. We invite you to visit the project website https://data.psychosensing.psnc.pl/popane/index.hml .

GitHub repository

Scripts for converting data from proprietary acquisition software formats into consistent CSV files, as well as IPython Jupyter Notebooks presenting how to load the data from POPANE CSV files into Python Pandas DataFrame structure are available at the following GitHub repository: https://github.com/psychosensing/popane-2021 .

Code availability

The code can be accessed on the public GitHub repository: https://github.com/psychosensing/popane-2021 . It is licensed under MIT OpenSource license, i.e., the permission is granted, free of charge, to obtaining a copy of this software and associated files (e.g., the Jupyter IPython Notebooks), subject to the following conditions: the copyright notice and the MIT license permission notice shall be included in all copies or substantial portions of the software based on the scripts we published.

Scripts that we used to transform the data from proprietary acquisition formats into coherent CSV files utilized Python 3.6 83 . The list of the specific modules and their versions is available in the “requirements.txt” file in the GitHub repository.

Jupyter Notebooks use Python version: 3.5.3, as well as the following Python modules: packages related to Jupyter Notebook: notebook module v. 6.1.4; jupyter-core module v. 4.6.3, jupyter-client v. 6.1.7; ipython v. 7.9.0; ipykernel v. 5.3.4 84 ; and a data organization and manipulation module – pandas v. 0.25.3 73 .

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Acknowledgements

The authors thank Michał Kosakowski, Jolanta Enko, Martyna Dziekan, Dariusz Drążkowski and other members of Psychophysiology and Health Lab at Adam Mickiewicz University for helping in data collection. The authors thanks Katarzyna Janicka ([email protected]) for creating Fig. 1. Preparation of this article was supported by the National Science Center (Poland) research grants (UMO-2017/25/N/HS6/00814; UMO-2012/05/B/HS6/00578; UMO-2013/11/N/HS6/01122; UMO-2014/15/B/HS6/02418; UMO-2014-15/N/HS6/04151; UMO-2015/17/N/HS6/02794; UMO-2016/21/B/ST6/01463) and doctoral scholarship (UMO-2019/32/T/HS6/00039) and by Faculty of Psychology and Cognitive Sciences, Adam Mickiewicz University research grant #18/11/2020.

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A.B. coded the software for the data preprocessing, developed the dataset, contributed to the technical validation, and composed the manuscript’s first draft. L.D.K. collaborated in the design of all the experimental setups, supervised the data collection and technical validation, secured funding for the work, and managed the project. M.Be. designed the experimental setup for Study 7, supported the data collection in all studies, verified the dataset, developed the dataset, contributed to the technical validation, composed the manuscript’s first draft, secured funding for the work, and managed the project. M.Bu. developed the dataset, contributed to the technical validation, and composed the manuscript’s first draft. S.K. supervised the data processing and technical validation. All authors critically reviewed and approved the final version of the manuscript.

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Behnke, M., Buchwald, M., Bykowski, A. et al. Psychophysiology of positive and negative emotions, dataset of 1157 cases and 8 biosignals. Sci Data 9 , 10 (2022). https://doi.org/10.1038/s41597-021-01117-0

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The development of Neuroscience has raised questions about the place of Physiological Psychology in both Psychology and Neuroscience. The present paper addresses the identity crisis of Physiological Psychology by focusing on the concept of the localization of function in the explanation of brain-behavior relationships. The physiological psychologist, dependent upon the reductionistic assumption that behavior can be explained by reduction to some brain event, and the notion that we have a firm understanding of behavioral events and processes, has turned to Neuroscience for both academic identity and research sustenance. But Neuroscience lacks a molar framework, and the consequence of the flight into Neuroscience has been the deterrence of integrative theorizing about brain-behavior relationships. Only through a return to the basic intellectual tradition of our discipline can we negate this trend. By attempting to identify and understand the natural fracture lines of complex adaptive behavioral functions, physiological psychologists can begin to develop the integrative theories that will foster an understanding of brain-behavioral relationships. Doing so has significant implications for what we teach, as well as for the role we play in the Psychology-Neuroscience endeavor of the future.

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This article is based upon papers presented as part of a symposium entitled “A question of identity: Physiological psychology’s place in neuroscience,” which was held during the 1983 annual meeting of the Southeastern Psychological Association in Atlanta, Georgia. Symposium participants and the titles of their presentations were: D. Shuttlesworth, “Issues underlying the identity crisis within physiological psychology”; D. Neill, “Physiological psychology’s place in neuroscience“; P. Ellen, “Physiological psychology: Alive and well within psychology“; and R. Levitt, “Physiological psychology and clinical neuropsychology.” Discussants were James Kalat and Walter Isaac.

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Shuttlesworth, D., Neill, D. & Ellen, P. Current Issues The place of Physiological Psychology in Neuroscience. Psychobiology 12 , 3–7 (1984). https://doi.org/10.3758/BF03332156

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Received : 17 February 1984

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Published : 04 November 2013

Issue Date : March 1984

DOI : https://doi.org/10.3758/BF03332156

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Psychological aging, depression, and well-being

Maria mitina.

1 Deep Longevity, Inc., Three Exchange Square, The Landmark, Hong Kong, China

Sergey Young

2 Longevity Vision Fund, New York, NY 10022, USA

Alex Zhavoronkov

3 Insilico Medicine, Hong Kong Science and Technology Park (HKSTP), Hong Kong, China

4 The Buck Institute for Research on Aging, Novato, CA 94945, USA

Aging is a multifactorial process, which affects the human body on every level and results in both biological and psychological changes. Multiple studies have demonstrated that a lower subjective age is associated with better mental and physical health, cognitive functions, well-being and satisfaction with life. In this work we propose a list of non-modifiable and modifiable factors that may possibly be influenced by subjective age and its changes across an individual’s lifespan. These factors can be used for a future development of individual psychological aging clocks, which may be utilized as a sensitive measure for health status and overall life satisfaction. Furthermore, recent progress in artificial intelligence and biomarkers of biological aging have enabled scientists to discover and evaluate the efficacy of potential aging- and disease-modifying drugs and interventions. We propose that biomarkers of psychological age, which are just as important as those for biological age, may likewise be used for these purposes. Indeed, these two types of markers complement one another. We foresee the development of a broad range of parametric and deep psychological and biopsychological aging clocks, which may have implications for drug development and therapeutic interventions, and thus healthcare and other industries.

INTRODUCTION

Like many other species, humans have a shorter lifespan in the absence of medical interventions [ 1 ]. Since the dawn of the 20 th century, life expectancy in developed countries has been steadily increasing primarily due to the decreases in child mortality but also due to the many advances in biotechnology and medicine [ 2 ]. Humans have had to adjust to this increase both as a society and at an individual level. Increasing life expectancy has led to substantial variability in the perception of age, as individuals may perceive themselves and others as substantially younger or older than their chronological age. The perception of subjective age may have profound effects on behavior and well-being, and is connected to an individual’s lifespan [ 3 ]. The socioemotional selectivity theory developed by Laura L. Carstensen at Stanford University, maintains that “the perception of time plays a fundamental role in the selection and pursuit of social goals” [ 4 ]. An extended perception of time may lead to knowledge-based motivations and choices. Conversely, when the perception of time is limited, a person may be motivated to preferentially make emotion-based decisions [ 5 ]. This theory and associated studies have highlighted the importance of the psychology of aging as a field and laid the foundation for studies of psychological and psychophysiological aging markers. While substantial progress has been made in identifying biomarkers of human biological aging, psychological aging is still poorly understood. There is a need for reliable tools for measuring and analyzing psychological aging, and methods for modulating longevity expectations and psychological aging states. In this paper we reflect upon the recent progress in the development of biomarkers of biological aging. We further provide a brief overview of the psychology of aging. We propose that this body of knowledge will lay the foundations for the development of next-generation biomarkers of psychological aging, dubbed psychological aging clocks, as well as deep multi-modal biopsychological and psychophysiological biomarkers of aging.

Numerous studies have demonstrated that molecular and phenotypic biomarkers may be used as effective tools for tracking healthy aging ( Table 1 ). Since 2016, multiple deep biomarkers of aging, identified using artificial intelligence, have been proposed. These include blood biochemistry-based clocks [ 6 ], transcriptomic and proteomic aging clocks [ 7 ], epigenetic aging clocks [ 8 ], microbiome aging clocks [ 9 ], photographic aging clocks [ 10 ], and many others. These clocks may be applied very broadly to industries that are dependent on consumer health and longevity, including the pharmaceutical and consumer industries [ 11 – 13 ].

These clocks can be used to assess the value of human data [ 14 ], perform data quality control [ 15 ], and many other applications. Substantial progress has been made in recent years in the use of artificial intelligence for drug discovery and biomarker development [ 16 , 17 ]. Additionally, neuroimaging techniques like magnetic resonance imaging (MRI) and electroencephalogram (EEG) may be used in studies of potential biomarkers for healthy brain aging.

However, as discussed earlier, developing psychological aging clocks is also of great importance. Unlike the biological features used in biological aging clocks, many modifiable psychological aging features are easily interpretable by individuals and scientific specialists. Furthermore, methods and protocols developed for psychological age reversal may be also used in biological research for biomarker development and establishing causality. Here we review the history and state of the art in psychological aging approaches and provide a perspective on the future of psychophysiology and the psychology of aging.

The study of psychological aging

When it comes to psychological health, a person's subjective psychological constructs may be more valuable than previously thought. Various studies have examined a number of subjective psychological concepts to understand psychological aging, including subjective age, age identity, the aging self, attitudes toward one’s own aging, self-perceptions of aging, and satisfaction with aging [ 29 ]. Historically, a single question has been used to formalize the concept of subjective age: “What age do you feel?” [ 30 , 31 ] ( Table 2 ). The answer is known as age identity, which is calculated as the difference between subjective age and chronological age [ 32 ]. Another approach for determining subjective age involves asking participants whether they feel psychologically and physically younger, older, or the same as their chronological age [ 33 ]. Further variations on this approach include asking participants to match themselves with a specific age group, such as middle-aged or older, or with a cognitive age (i.e., feel-age, look-age, do-age, and interest-age [ 34 ]. These classifications require greater implementation in longitudinal studies. In a recent study by Veenstra and colleagues [ 35 ], an analysis of longitudinal national survey data showed that a desire to be younger than one’s chronological age may be associated with lower life satisfaction and lower physical activity in the second half of a person’s life. Thus, enhanced life enjoyment is correlated with higher age satisfaction. These data raise the question of what an individual’s ideal age is, which can be interrogated by the following prompt: “If you could choose your age, what age would you like to be?” Another measure, which could be applied in clinical practice are questions about visual perceived age. This approach defines the age of participants by the perception of digital photos or physical appearance. The Longitudinal Study of Aging Danish Twins demonstrated that perceived age estimated from photographs could be used as a predictor of mortality in the volunteers. In our review we employ the term “perceived age” as related to the subjective perception of age, as opposed to visual perception [ 36 ].

An individual’s perceived age may influence how they overcome illness and cope with symptoms; for example, a positive view on life is linked to positive health outcomes [ 3 ]. The authors of this study further suggested that feeling younger may be an adaptive strategy in society. However, the link between mental subjective age and physiology is still not understood. Westerhof and Wurm proposed a hierarchical model that linked subjective age, psychological resources, and health. This model suggested that feeling the same as, or younger than, one’s chronological age may be associated with improved health. Alternatively, the reverse of this model may be true: better health drives a younger subjective age. In addition, the interoceptive hypothesis proposed that physical and cognitive functions decrease with age, a phenomenon that is related to an individual’s awareness of age-related changes [ 37 , 38 ]. Thus, perceiving oneself as subjectively younger than one’s chronological age may influence age-related biological changes.

Numerous studies have shown that adults have a tendency to feel younger than their calendar age, and this difference increases with calendar age [ 39 , 40 ]. For instance, people older than 25 years exhibited a younger subjective age [ 41 ]. In a series of studies, Weiss and colleagues also found that when older participants were confronted with negative age-related information, they perceived themselves as more similar to younger, rather than older, individuals and distanced themselves from their same-age peers [ 42 , 43 ]. Studies comparing American and German populations demonstrated that adults felt younger than their calendar age, although Germans noticed an older subjective age than Americans [ 44 ]. This finding may show the youth-centeredness of American culture compared to Europe. Nevertheless, the stereotype embodiment theory [ 45 ] proposes that as adults age, they may increasingly accept society’s stereotypical expectations about their functional capacity, which in turn may influence their actual productivity and health. Thus, subjective age might depend on the socio-cultural values in a society.

Bergland and colleagues [ 46 ] demonstrated no significant differences in subjective age based on gender. However, the authors reported that men in multiple age groups (40–49 years, 50–59 years) with less education felt more youthful than those with more education. These results are, however, contradicted by previous studies where an older perceived age was correlated with fewer years of education [ 32 , 47 ]. However, Kaufman and Elder [ 48 ] demonstrated that education has no significant influence on the perception of age. Accordingly, age perception may be associated with stigmatization regarding a person’s level of education and certain professional areas.

To address this issue in greater detail, we previously conducted a survey of the International Employee Benefits Association with a large, international industry group [ 49 ]. Industry professionals were employed by consulting, insurance, pension, and other companies. The surveyed individuals are experts in predicting life expectancy and mortality trends in the future. The assumptions for the mortality tables developed by the actuaries may have profound implications on insurance companies, governments, and the global economy since every extra year of an unfunded pension or a medical plan may result in billions or even trillions of dollars in liabilities. To our surprise, the longevity expectations of this group were conservative and did not account for future breakthroughs in biomedicine. This is notable, as this group of people is responsible for decisions that may affect the global economy and society. Thus, adjusting the psychological age and longevity expectations of this group of people may have a substantial positive impact.

Subjective, chronological, and biological age

In a meta-analysis of 19 longitudinal studies, it was reported that subjective aging has a small but significant effect on health, health behaviors, and survival [ 3 ]. Stephan and colleagues showed an association between older subjective age and higher systemic inflammation and obesity [ 50 ]. Additional studies by Thyagarajan and colleagues [ 51 ] found decreased albumin concentrations in participants who felt younger. In contrast, the researchers observed higher levels of albumin in volunteers who felt older compared to a reference group. This study also showed that the prevalence of a clinically significant rise in liver enzymes, such as alanine aminotransferase, was significantly lower among the participants reporting younger subjective ages. Moreover, the researchers demonstrated that levels of cystatin C were also reduced among those who felt younger when compared with the control group. No correlations between lipids, glucose, or C-reactive protein (an inflammatory marker) and subjective age were identified. These results were partly further confirmed by Stephan and colleagues [ 52 ].

Perceived older age was also found to correlate with certain diseases, such as diabetes [ 53 ]. Moreover, subjective age was related to markers of biological age, including peak expiratory flow and grip strength [ 54 ]. Longitudinal studies have also shown that poor health, lower physical activity, body mass index, and the subjective experience of aging may be associated with cognitive abilities in later life [ 55 ].

Neurophysiology and subjective age may also be connected. For example, elderly individuals that reported a subjective age similar to or younger than their actual chronological age exhibited a higher volume of grey matter in the inferior frontal gyrus and the superior temporal gyrus; this study also found that subjective age was a predictor for younger brain age [ 37 ]. However, additional studies related to subjective age and neurophysiological mechanisms of aging are still required.

Subjective age and stress

The central nervous system (CNS), the endocrine system and the immune system are complex and interconnected. Previous research suggested that stressful life events may negatively influence aspects of immune system function [ 56 ]. Psychological stress may increase the production of pro-inflammatory cytokines that are related to a variety of age-related diseases. For instance, catecholamines (adrenaline and noradrenaline), adrenocorticotropic hormone, cortisol, growth hormone and prolactin are all correlated with distress and adverse emotions [ 56 ]. Furthermore, age-related diseases may exacerbate the influence of stress or the effects of medical disabilities on elderly persons. Moreover, extreme stress early in life may have a long-lasting influence on the CNS, the endocrine system and the immune system.

Day-to-day variability in subjective age, such as feeling older than one’s chronological age, is associated with health issues and routine stress [ 57 ]. Indeed, researchers have suggested that everyday subjective age doesn’t vary significantly with time in the absence of other factors.

Solomon, Helvitz, and Zerach [ 58 ] showed that veterans suffering from post-traumatic stress disorder (PTSD) exhibited an older subjective age compared to veterans without PTSD. Furthermore, in a study by Palgi [ 59 ], it was demonstrated that higher levels of post-traumatic stress symptoms (PTSS) were both linearly and curvilinearly associated with a possibility of higher post-traumatic growth (PTG). PTG is defined as the positive changes that occur after trauma [ 60 , 61 ]. Subjective age and perceived distance-to-death mediated this association in a linear way. Furthermore, participants who reported younger subjective age and further distance-to-death exhibited the strongest association. This was also confirmed in a previous study [ 62 ]; in contrast, the combined experience of feeling close to death and older subjective age were correlated with an increased degree of stressful events. Moreover, the effect of perceived distance-to-death on stress was softened by a perceived younger age.

In another study, ex-prisoners of war (ex-POWS) demonstrated a higher subjective age than healthy participants [ 63 ]. Additionally, ex-POWs with PTSD reported a higher subjective age than ex-POWs and volunteers without PTSD. PTSS and health measures were predictors of subjective age. Strong interactions between PTSS and health measures suggest that health only predicts subjective age in the presence of high PTSS.

These data have been corroborated by Lahav and colleagues at the molecular level by measuring telomere length, which suggested that feeling older is associated with cellular senescence [ 64 ]. Telomeres are DNA–protein complexes that cap chromosomal ends, promoting chromosomal stability. Telomeres shorten with age and thus telomere length often serves as a biomarker of cellular aging. Perceived older age was related to shorter telomeres, beyond the effect of chronological age. Variations in perceived age also mediated connections between depression and shorter telomeres.

In addition, holocaust survival and PTSD are related to attitudes toward aging and subjective age [ 65 ]. Thus, these numerous investigations show that subjective age can be used as a tool for clinical interventions in traumatized patients and for patients suffering from depressive episodes. Further investigation will be required to determine the precise interactions between these biological and psychological factors.

Subjective age and depression

Depression is one of the most common mental illnesses worldwide. Depression may include behavioral, somatic, and cognitive impairments, and a loss of interest. Furthermore, depression can occur at any point during a human’s lifespan, and major depressive episodes (MDE) may relapse. More than half of all MDE incidents occur in individuals who experience their first MDE later in life [ 66 ]. Depression is linked to increased cortisol levels, and can thus receive negative input from the immune system. In addition, patients with depression may exhibit a perceived state of anxiety and feelings of fear [ 67 ].

Keys and Westerhof [ 68 ] have shown a link between self-perceptions of aging, chronological age, and mental health. The authors found that younger subjective age positively impacts mental health, produces a lower risk of MDE, and results in flourishing mental health (FMH). Additionally, the desire to be younger was correlated with a lower incidence of FMH and unrelated to MDE.

In a longitudinal study by Choi and Dinitto [ 69 ], an older perceived age predicted higher depressive symptoms in the future. However, younger subjective age did not produce reduced depressive symptoms in a follow-up study. Furthermore, a longitudinal study of depression and chronic illness found that older subjective age can be a risk factor for physical morbidity and depression in the future [ 70 ]. Thus, psychological states may modulate health ( Figure 1 ).

The mind-body connection. Biological age and subjective age are connected with a variety of diseases and may be directly linked.

Subjective age and cognitive functioning

Subjective perception of cognitive dysfunction may be associated with the early stages of dementia or morbid changes in the nervous system [ 37 , 71 , 72 ]. Likewise, younger subjective age is associated with better memory functioning [ 73 ]. Stephan and colleagues [ 55 ] showed that younger age feelings were associated with improved cognitive functioning 10 years later, which were determined by the strength of episodic memory and executive functioning assessments. However, this study estimated a follow-up in participants without a baseline. Furthermore, the perception of younger age was found to be related to personality traits such as openness, conscientiousness, agreeableness, and extraversion [ 74 ]. In addition to chronological age, older subjective age was correlated with a higher risk of dementia in patients over 65 during a four-year period. The authors of this study noted that this connection was caused by depressive symptoms [ 75 ]. Taken together, subjective age and cognitive abilities may be associated.

Subjective age and mortality

Stephan and colleagues showed a relationship between subjective age and the probability of mortality in three large samples [ 76 ]. In this study, participants exhibited on average a 15% to 16% lower subjective age as compared to their calendar age. A subjective age of around 8, 11, and 13 years older in the three samples was correlated with an 18%, 29%, and 25% higher risk of mortality, respectively. These results were supported by a meta-analysis of the three samples. The authors demonstrated that chronic diseases, lack of physical activity, and cognitive issues, but not symptoms of depression, predicted the connection between subjective age and mortality. The authors concluded that a correlation exists between an older subjective age and a higher risk of mortality for adults. It was also reported that age identity could predict all-cause and cardiovascular mortality over an eight-year period. These results indicate that subjective age can be used as a biopsychosocial marker of aging. People with perceived older ages may be a potential audience for psychological interventions to modify well-being and attitudes toward aging [ 77 ].

Subjective age and well-being

Excellent reviews on the topic of subjective well-being and related terms have been written [ 78 , 79 ]. In this review we use the term “well-being” as it relates to satisfaction with life, positive and negative affect, etc. [ 80 ]. People who feel psychologically younger than their chronological age are more satisfied with their lives than those who are psychologically older [ 81 ]. Psychologically younger people have more resources, which are likely to include better mental and physical health, cognitive abilities, resilience to stress, biological age, and longevity. Mock and Eibach demonstrated that perceiving oneself as older predicts lower life satisfaction, an effect that may depend on aging attitudes [ 82 ]. They further found a relationship between higher negative affects, lower life satisfaction, and less advantageous aging attitudes.

There are many factors that influence psychological age and how it is related to subjective well-being ( Figure 2 ). Some factors, so called non-modifiable factors, cannot be easily changed with behavioral modifications or therapeutic interventions. Non-modifiable factors include genetic predisposition, parental age, family members’ age of death, children’s’ age, retirement age, and average life expectancy in the country. However, there are many more factors that can be modified to reduce psychological age. These factors include health status and disabilities, physical activity, longevity expectations, education, biomedical knowledge, work, environment, psychological support, social relationships, and personal beliefs. All these factors may affect psychological age, which in turn may influence overall satisfaction with life. We propose that these modifiable factors could be used for the development of psychological aging clocks, which will require further study.

List of modifiable and non-modifiable factors that may influence psychological age.

Experimental tests of subjective age

Few studies have utilized experimental analyses to test the theoretical framework of subjective age. Stephan and colleagues showed that individuals who received positive feedback while performing a grip task experienced a younger subjective age compared to a control group without feedback [ 83 ]. In another study, Kutter-Grugn and Hess demonstrated that stereotypical negative thinking regarding age may induce older subjective age states [ 84 ]. Future work on psychological age should employ similar experimental manipulations.

Therefore, we would like to propose a series of experimental case studies to carry out in the future, in which some of the modifiable factors described in Figure 2 are manipulated in order to influence psychological age. First, we suggest that psychological affirmations could be used as an intervention to modify longevity and health expectations by 10 years. In a second case study, we could modulate participants’ responses in an experimental group by including people younger than her/him in that same group. Finally, we could design an experimental workout programme with instructions stating that the exercises would lead to feeling younger. This last experimental study was inspired by research into the placebo effect and rethinking by Alia Crum. Crum and Langer had an experimental group of hotel workers believe that their work was actually related to physical exercise and had a positive effect on health, whereas a control group of workers at the same hotel received no such instructions [ 85 ]. In this case, the experimental group showed a decrease in weight, blood pressure, waist-to-hip ratio, and body mass index after 4 weeks.

Trends analysis and grants

A basic search of trends analysis ( Figure 3 ) was performed using Google Trends ( https://trends.google.com/ ) using the keywords “psychological age” and “biological age ”. This analysis demonstrated that, despite the increasing popularity of biological aging clocks among scientists, the topic of psychological aging is substantially less popular among the general public. Considering the link between subjective aging, health, and mental state, substantially more resources should be committed to psychological aging research. Psychological aging is just as important as biological aging and requires the development of parametric and deep psychological aging clocks to track the rate of psychological aging and to identify effective interventions to modulate psychological aging.

Interest over time. The blue line demonstrates interest in psychological age, while the red line indicates interest in biological age. The numbers represent search interest relative to the highest point on the chart over time. The value of 100 is the peak popularity for the term, while a value of 50 indicates that the term is half as popular. A score of 0 means there was not enough data for the term. Source: https://trends.google.com ./

In addition, an analysis of the funding for psychological aging studies was performed using the open online grants search engine PharmaCognitive ( http://www.pharmacognitive.com ). This search engine was built using similar techniques as the International Aging Research Portfolio (IARP) [ 86 ], albeit with a significantly larger number of data sources and data types. Using the search query "Psychological Aging" ( Figure 4 ) revealed that the amount of funding related to psychological aging research is steadily increasing and is likely to result in substantial publication activity and data availability in the coming years. The most popular topics related to funding are neurobiology of aging, psychological well-being, cognitive decline, and Alzheimer’s disease.

Funding by years related to the topic of “Psychological Aging”. Source: https://www.pharmacognitive.com/ .

There is a substantial research effort directed towards the development and analysis of A National Longitudinal Study of Health & Well-Being Midlife in the United States (MIDUS, http://midus.wisc.edu/ ). MIDUS includes psycho-social, health, cognitive, and biomarkers measures, as well as neuroscience data (MRI, EEG). Research via PharmaCognitive showed that over 218 grants were awarded with “MIDUS” in the grant title. The majority of the grants were awarded by the National Institute on Aging (NIA) for studies supervised by Doctor Carol Ryff at the University of Wisconsin Madison, who was identified as the main Key Opinion Leader (KOL) in the field. In addition, there were more than 300 publications with “MIDUS” as a keyword. In addition to MIDUS, similar studies have been carried out in other countries, such as Midlife in Japan (MIDJA). The principal aim was to compare MIDJA with MIDUS to investigate the influence of psychosocial factors on the health of mid- and later-life adults in Japan and the United States.

In addition, the Leipzig Study for Mind-Body-Emotion Interactions (LEMON, http://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html ) features datasets for healthy participants from a number of different age groups [ 87 ]. LEMON is a part of the larger Max Planck Institute Leipzig Mind-Brain-Body database, and contains psychological and physiological data, including EEG and MRI measures. There is a similar public resource for data on aging in America, which has existed since 1900, called the Health Retirement Study (HRS, https://hrs.isr.umich.edu/ ). This study includes data on cognition, health, psycho-social, biomarkers, and genetic data. There are more than 5000 publications related to HRS.

As indicated in this review, a broad literature suggests that there is a relationship between age identity and health, mental states, cognitive functioning, longevity, and well-being. Increasing human productive longevity by slowing down or even reversing biological and psychological aging will help accelerate economic growth in major developed countries [ 88 , 89 ]. Subjective aging is determined by various parameters such as health changes, personal experiences, social relationships, and cultural values. Given the strong connection between aging and general factors of well-being, promoting a positive attitude towards one’s own aging may be an important aim for public health efforts and clinics.

Despite numerous studies on subjective age, only a limited number of related biomarkers have been examined. For instance, the combined influence of perceived subjective age, epigenetic factors, and biological systems, such as the central nervous system, peripheral system, and immune system, will require more precise research. A complex approach may shed light on age-related changes and the risk of future mental illnesses, which can additionally be associated with productive functioning.

Multiple studies have demonstrated that lower subjective age is associated with better mental and physical health, cognitive functions, and satisfaction with life. The ability to precisely measure subjective or psychological age and identify the key modifiable factors, evaluate their importance, and analyze the correlations between these factors may help improve the quality of life of patients and the general population. Future investigations are needed to further contribute to the understanding of the practical implementation of such measures. In this review we also propose a list of non-modifiable and modifiable factors, which may be influenced by subjective age and its changes across an individual’s lifespan. We intend to use these modifiable psychological factors, in combination with biological factors, as important features for the development of psychological aging clocks.

In addition, in order to increase individuals’ resilience to stress and achieve positive behavioral changes, new tools for evaluating biopsychological profiles should be developed. Recent advances in artificial intelligence allow for the development of multi-modal biomarkers of aging. However, the majority of these efforts are focused on biological aging clocks. We speculate that the development of psychological aging clocks using deep learning techniques may be just as impactful and may help validate and improve these deep learning approaches, as psychological survey, lifestyle, and behavioral data is usually more interpretable. We foresee the development of many types of deep psychological, psychophysiological, and biopsychological aging clocks using machine learning techniques, and believe they may one day be used as standard tools in psychiatry, longevity research, and in a broad range of applications across many industries.

AUTHOR CONTRIBUTIONS: AZ originated the idea and performed research. MM performed research. AZ and MM wrote the paper.

CONFLICTS OF INTEREST: These authors declare no conflicts of interest.

FUNDING: The work was self-funded.

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50+ Research Topics for Psychology Papers

How to Find Psychology Research Topics for Your Student Paper

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

• Specific Branches of Psychology
• Topics Involving a Disorder or Type of Therapy
• Human Cognition
• Human Development
• Critique of Publications
• Famous Experiments
• Historical Figures
• Specific Careers
• Case Studies
• Literature Reviews
• Your Own Study/Experiment

Are you searching for a great topic for your psychology paper ? Sometimes it seems like coming up with topics of psychology research is more challenging than the actual research and writing. Fortunately, there are plenty of great places to find inspiration and the following list contains just a few ideas to help get you started.

Finding a solid topic is one of the most important steps when writing any type of paper. It can be particularly important when you are writing a psychology research paper or essay. Psychology is such a broad topic, so you want to find a topic that allows you to adequately cover the subject without becoming overwhelmed with information.

I can always tell when a student really cares about the topic they chose; it comes through in the writing. My advice is to choose a topic that genuinely interests you, so you’ll be more motivated to do thorough research.

In some cases, such as in a general psychology class, you might have the option to select any topic from within psychology's broad reach. Other instances, such as in an  abnormal psychology  course, might require you to write your paper on a specific subject such as a psychological disorder.

As you begin your search for a topic for your psychology paper, it is first important to consider the guidelines established by your instructor.

Research Topics Within Specific Branches of Psychology

The key to selecting a good topic for your psychology paper is to select something that is narrow enough to allow you to really focus on the subject, but not so narrow that it is difficult to find sources or information to write about.

One approach is to narrow your focus down to a subject within a specific branch of psychology. For example, you might start by deciding that you want to write a paper on some sort of social psychology topic. Next, you might narrow your focus down to how persuasion can be used to influence behavior .

Other social psychology topics you might consider include:

• Prejudice and discrimination (i.e., homophobia, sexism, racism)
• Social cognition
• Person perception
• Social control and cults
• Persuasion, propaganda, and marketing
• Attraction, romance, and love
• Nonverbal communication
• Prosocial behavior

Psychology Research Topics Involving a Disorder or Type of Therapy

Exploring a psychological disorder or a specific treatment modality can also be a good topic for a psychology paper. Some potential abnormal psychology topics include specific psychological disorders or particular treatment modalities, including:

• Eating disorders
• Borderline personality disorder
• Seasonal affective disorder
• Schizophrenia
• Antisocial personality disorder
• Profile a  type of therapy  (i.e., cognitive-behavioral therapy, group therapy, psychoanalytic therapy)

Topics of Psychology Research Related to Human Cognition

Some of the possible topics you might explore in this area include thinking, language, intelligence, and decision-making. Other ideas might include:

• False memories
• Speech disorders
• Problem-solving

Topics of Psychology Research Related to Human Development

In this area, you might opt to focus on issues pertinent to  early childhood  such as language development, social learning, or childhood attachment or you might instead opt to concentrate on issues that affect older adults such as dementia or Alzheimer's disease.

Some other topics you might consider include:

• Language acquisition
• Media violence and children
• Learning disabilities
• Gender roles
• Child abuse
• Prenatal development
• Parenting styles
• Aspects of the aging process

Do a Critique of Publications Involving Psychology Research Topics

One option is to consider writing a critique paper of a published psychology book or academic journal article. For example, you might write a critical analysis of Sigmund Freud's Interpretation of Dreams or you might evaluate a more recent book such as Philip Zimbardo's  The Lucifer Effect: Understanding How Good People Turn Evil .

Professional and academic journals are also great places to find materials for a critique paper. Browse through the collection at your university library to find titles devoted to the subject that you are most interested in, then look through recent articles until you find one that grabs your attention.

Topics of Psychology Research Related to Famous Experiments

There have been many fascinating and groundbreaking experiments throughout the history of psychology, providing ample material for students looking for an interesting term paper topic. In your paper, you might choose to summarize the experiment, analyze the ethics of the research, or evaluate the implications of the study. Possible experiments that you might consider include:

• The Milgram Obedience Experiment
• The Stanford Prison Experiment
• The Little Albert Experiment
• Pavlov's Conditioning Experiments
• The Asch Conformity Experiment
• Harlow's Rhesus Monkey Experiments

Topics of Psychology Research About Historical Figures

One of the simplest ways to find a great topic is to choose an interesting person in the  history of psychology  and write a paper about them. Your paper might focus on many different elements of the individual's life, such as their biography, professional history, theories, or influence on psychology.

While this type of paper may be historical in nature, there is no need for this assignment to be dry or boring. Psychology is full of fascinating figures rife with intriguing stories and anecdotes. Consider such famous individuals as Sigmund Freud, B.F. Skinner, Harry Harlow, or one of the many other  eminent psychologists .

Psychology Research Topics About a Specific Career

​Another possible topic, depending on the course in which you are enrolled, is to write about specific career paths within the  field of psychology . This type of paper is especially appropriate if you are exploring different subtopics or considering which area interests you the most.

In your paper, you might opt to explore the typical duties of a psychologist, how much people working in these fields typically earn, and the different employment options that are available.

Topics of Psychology Research Involving Case Studies

One potentially interesting idea is to write a  psychology case study  of a particular individual or group of people. In this type of paper, you will provide an in-depth analysis of your subject, including a thorough biography.

Generally, you will also assess the person, often using a major psychological theory such as  Piaget's stages of cognitive development  or  Erikson's eight-stage theory of human development . It is also important to note that your paper doesn't necessarily have to be about someone you know personally.

In fact, many professors encourage students to write case studies on historical figures or fictional characters from books, television programs, or films.

Psychology Research Topics Involving Literature Reviews

Another possibility that would work well for a number of psychology courses is to do a literature review of a specific topic within psychology. A literature review involves finding a variety of sources on a particular subject, then summarizing and reporting on what these sources have to say about the topic.

Literature reviews are generally found in the  introduction  of journal articles and other  psychology papers , but this type of analysis also works well for a full-scale psychology term paper.

Topics of Psychology Research Based on Your Own Study or Experiment

Many psychology courses require students to design an actual psychological study or perform some type of experiment. In some cases, students simply devise the study and then imagine the possible results that might occur. In other situations, you may actually have the opportunity to collect data, analyze your findings, and write up your results.

Finding a topic for your study can be difficult, but there are plenty of great ways to come up with intriguing ideas. Start by considering your own interests as well as subjects you have studied in the past.

Online sources, newspaper articles, books , journal articles, and even your own class textbook are all great places to start searching for topics for your experiments and psychology term papers. Before you begin, learn more about  how to conduct a psychology experiment .

What This Means For You

After looking at this brief list of possible topics for psychology papers, it is easy to see that psychology is a very broad and diverse subject. While this variety makes it possible to find a topic that really catches your interest, it can sometimes make it very difficult for some students to select a good topic.

If you are still stumped by your assignment, ask your instructor for suggestions and consider a few from this list for inspiration.

• Hockenbury, SE & Nolan, SA. Psychology. New York: Worth Publishers; 2014.
• Santrock, JW. A Topical Approach to Lifespan Development. New York: McGraw-Hill Education; 2016.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

61 intriguing psychology research topics to explore

Last updated

11 January 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Psychology is an incredibly diverse, critical, and ever-changing area of study in the medical and health industries. Because of this, it’s a common area of study for students and healthcare professionals.

We’re walking you through picking the perfect topic for your upcoming paper or study. Keep reading for plenty of example topics to pique your interest and curiosity.

• How to choose a psychology research topic

Exploring a psychology-based topic for your research project? You need to pick a specific area of interest to collect compelling data. 

Use these tips to help you narrow down which psychology topics to research:

Focus on a particular area of psychology

The most effective psychological research focuses on a smaller, niche concept or disorder within the scope of a study. 

Psychology is a broad and fascinating area of science, including everything from diagnosed mental health disorders to sports performance mindset assessments. 

This gives you plenty of different avenues to explore. Having a hard time choosing? Check out our list of 61 ideas further down in this article to get started.

Read the latest clinical studies

Once you’ve picked a more niche topic to explore, you need to do your due diligence and explore other research projects on the same topic. 

This practice will help you learn more about your chosen topic, ask more specific questions, and avoid covering existing projects. 

For the best results, we recommend creating a research folder of associated published papers to reference throughout your project. This makes it much easier to cite direct references and find inspiration down the line.

Find a topic you enjoy and ask questions

Once you’ve spent time researching and collecting references for your study, you finally get to explore. 

Whether this research project is for work, school, or just for fun, having a passion for your research will make the project much more enjoyable. (Trust us, there will be times when that is the only thing that keeps you going.) 

Now you’ve decided on the topic, ask more nuanced questions you might want to explore. 

If you can, pick the direction that interests you the most to make the research process much more enjoyable.

• 61 psychology topics to research in 2024

Need some extra help starting your psychology research project on the right foot? Explore our list of 61 cutting-edge, in-demand psychology research topics to use as a starting point for your research journey.

• Psychology research topics for university students

As a university student, it can be hard to pick a research topic that fits the scope of your classes and is still compelling and unique. 

Here are a few exciting topics we recommend exploring for your next assigned research project:

Mental health in post-secondary students

Seeking post-secondary education is a stressful and overwhelming experience for most students, making this topic a great choice to explore for your in-class research paper. 

Examples of post-secondary mental health research topics include:

Student mental health status during exam season

Mental health disorder prevalence based on study major

The impact of chronic school stress on overall quality of life

The impacts of cyberbullying

Cyberbullying can occur at all ages, starting as early as elementary school and carrying through into professional workplaces. 

Examples of cyberbullying-based research topics you can study include:

The impact of cyberbullying on self-esteem

Common reasons people engage in cyberbullying 

Cyberbullying themes and commonly used terms

Cyberbullying habits in children vs. adults

The long-term effects of cyberbullying

• Clinical psychology research topics

If you’re looking to take a more clinical approach to your next project, here are a few topics that involve direct patient assessment for you to consider:

Chronic pain and mental health

Living with chronic pain dramatically impacts every aspect of a person’s life, including their mental and emotional health. 

Here are a few examples of in-demand pain-related psychology research topics:

The connection between diabetic neuropathy and depression

Neurological pain and its connection to mental health disorders

Efficacy of meditation and mindfulness for pain management

The long-term effects of insomnia

Insomnia is where you have difficulty falling or staying asleep. It’s a common health concern that impacts millions of people worldwide. 

This is an excellent topic because insomnia can have a variety of causes, offering many research possibilities. 

Here are a few compelling psychology research topics about insomnia you could investigate:

The prevalence of insomnia based on age, gender, and ethnicity

Insomnia and its impact on workplace productivity

The connection between insomnia and mental health disorders

Efficacy and use of melatonin supplements for insomnia

The risks and benefits of prescription insomnia medications

Lifestyle options for managing insomnia symptoms

The efficacy of mental health treatment options

Management and treatment of mental health conditions is an ever-changing area of study. If you can witness or participate in mental health therapies, this can make a great research project. 

Examples of mental health treatment-related psychology research topics include:

The efficacy of cognitive behavioral therapy (CBT) for patients with severe anxiety

The benefits and drawbacks of group vs. individual therapy sessions

Music therapy for mental health disorders

Electroconvulsive therapy (ECT) for patients with depression 

• Controversial psychology research paper topics

If you are looking to explore a more cutting-edge or modern psychology topic, you can delve into a variety of controversial and topical options:

The impact of social media and digital platforms

Ever since access to internet forums and video games became more commonplace, there’s been growing concern about the impact these digital platforms have on mental health. 

Examples of social media and video game-related psychology research topics include:

The effect of edited images on self-confidence

How social media platforms impact social behavior

Video games and their impact on teenage anger and violence

Digital communication and the rapid spread of misinformation

The development of digital friendships

Psychotropic medications for mental health

In recent years, the interest in using psychoactive medications to treat and manage health conditions has increased despite their inherently controversial nature. 

Examples of psychotropic medication-related research topics include:

The risks and benefits of using psilocybin mushrooms for managing anxiety

The impact of marijuana on early-onset psychosis

Childhood marijuana use and related prevalence of mental health conditions

Ketamine and its use for complex PTSD (C-PTSD) symptom management

The effect of long-term psychedelic use and mental health conditions

• Mental health disorder research topics

As one of the most popular subsections of psychology, studying mental health disorders and how they impact quality of life is an essential and impactful area of research. 

While studies in these areas are common, there’s always room for additional exploration, including the following hot-button topics:

Anxiety and depression disorders

Anxiety and depression are well-known and heavily researched mental health disorders. 

Despite this, we still don’t know many things about these conditions, making them great candidates for psychology research projects:

Social anxiety and its connection to chronic loneliness

C-PTSD symptoms and causes

The development of phobias

Obsessive-compulsive disorder (OCD) behaviors and symptoms

Depression triggers and causes

Self-care tools and resources for depression

The prevalence of anxiety and depression in particular age groups or geographic areas

Bipolar disorder

Bipolar disorder is a complex and multi-faceted area of psychology research. 

Use your research skills to learn more about this condition and its impact by choosing any of the following topics:

Early signs of bipolar disorder

The incidence of bipolar disorder in young adults

The efficacy of existing bipolar treatment options

Bipolar medication side effects

Cognitive behavioral therapy for people with bipolar 

Schizoaffective disorder

Schizoaffective disorder is often stigmatized, and less common mental health disorders are a hotbed for new and exciting research. 

Here are a few examples of interesting research topics related to this mental health disorder:

The prevalence of schizoaffective disorder by certain age groups or geographic locations

Risk factors for developing schizoaffective disorder

The prevalence and content of auditory and visual hallucinations

Alternative therapies for schizoaffective disorder

• Societal and systematic psychology research topics

Modern society’s impact is deeply enmeshed in our mental and emotional health on a personal and community level. 

Here are a few examples of societal and systemic psychology research topics to explore in more detail:

Access to mental health services

While mental health awareness has risen over the past few decades, access to quality mental health treatment and resources is still not equitable. 

This can significantly impact the severity of a person’s mental health symptoms, which can result in worse health outcomes if left untreated. 

Explore this crucial issue and provide information about the need for improved mental health resource access by studying any of the following topics:

Rural vs. urban access to mental health resources

Access to crisis lines by location

Wait times for emergency mental health services

Inequities in mental health access based on income and location

Insurance coverage for mental health services

Systemic racism and mental health

Societal systems and the prevalence of systemic racism heavily impact every aspect of a person’s overall health.

Researching these topics draws attention to existing problems and contributes valuable insights into ways to improve access to care moving forward.

Examples of systemic racism-related psychology research topics include: 

Access to mental health resources based on race

The prevalence of BIPOC mental health therapists in a chosen area

The impact of systemic racism on mental health and self-worth

Racism training for mental health workers

The prevalence of mental health disorders in discriminated groups

LGBTQIA+ mental health concerns

Research about LGBTQIA+ people and their mental health needs is a unique area of study to explore for your next research project. It’s a commonly overlooked and underserved community.

Examples of LGBTQIA+ psychology research topics to consider include:

Mental health supports for queer teens and children

The impact of queer safe spaces on mental health

The prevalence of mental health disorders in the LGBTQIA+ community

The benefits of queer mentorship and found family

Substance misuse in LQBTQIA+ youth and adults

• Collect data and identify trends with Dovetail

Psychology research is an exciting and competitive study area, making it the perfect choice for projects or papers.

Take the headache out of analyzing your data and instantly access the insights you need to complete your next psychology research project by teaming up with Dovetail today.

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