I checked 15 psychology journals on Friday, September 25, 2026 using the Crossref API. For the period September 18 to September 24, I found 47 new paper(s) in 10 journal(s).

Advances in Methods and Practices in Psychological Science

Pairmutate: A Web Application for Testing Dyadic Associations Through Comparison With Pseudo Dyads
Chiara Carlier, Liesse Frérart, Peter Kuppens, Eva Ceulemans
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Recently, a lot of attention in the behavioral sciences has been going to how feelings, thoughts, or behaviors overlap, covary, or synchronize across time within dyadic relationships, such as romantic couples or parent–child pairs. This is often investigated by computing and testing an association measure within each dyad. However, two individuals may demonstrate similar responses and thus association simply because they are in the same experimental conditions, have similar social or cultural backgrounds, or just share basic biological functions rather than that the observed similarity can be attributed to true interpersonal processes. This “common input” phenomenon is known to increase the probability of false positives when testing dyadic associations. Permutation testing allows one to account for it by comparing associations based on data from true dyads with associations based on data from pseudo dyads. Pseudo dyads are formed by coupling partners from different dyads in the sample. Although this approach has been proven valuable in dyadic research, it is conceptually and computationally complex and thus not readily accessible. In this tutorial, we explain the logic behind testing dyadic associations using pseudo dyads and introduce and demonstrate a newly developed web application called “Pairmutate” for this purpose.
Finding the Fingerprints of Generative Artificial Intelligence in Psychology Publications
Elouise Botes, Ziwen Teuber, Nora Vitali, Jean-Marc Dewaele, Joanne Colling
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Generative artificial intelligence (AI) and large language models (LLMs) in particular have become embedded in the research process such that scientists are increasingly using LLMs to assist with manuscript drafting. However, the extent to which LLMs are being used in psychology-research writing is largely unexplored. In this study, we investigated the presence of AI-associated linguistic markers in psychology publications between 2018 and 2025. Using a corpus of 512,970 abstracts across 975 journals, we analyzed the relative frequencies of 28 words previously identified as overrepresented in AI-assisted writing. Results showed a small gradual increase in these stylistic markers from 2018 to 2022, followed by a marked acceleration in their use after the release of ChatGPT and relative frequencies increasing in 2023, 2024, and 2025. Counterfactual modeling suggested that at least 16% of 2025 abstracts contained linguistic traces consistent with LLM editing. Group-level analyses revealed that increases were not confined to lower-prestige outlets; higher-ranked journals, major publishers, and both English- and non-English-speaking countries all showed significant growth in AI-associated word use. These findings point to large-scale shifts in academic writing that are consistent with the growing influence of LLMs, although such markers provide indirect rather than definitive evidence of their use.
Qualitative Researchers Can and Do Generalize: Generalizability Claims in Qualitative Psychological Research
Abby I. Person, Moin Syed
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A common critique of qualitative research is that it lacks generalizability, and a common response made by qualitative researchers is that qualitative research is not intended to be generalizable. Some have argued that because of varying epistemological assumptions within qualitative research, qualitative research is not capable of statistical-probabilistic generalization but is capable of analytic generalizability and transferability. The purpose of the current study was to explore generalizability in qualitative research. We first examined how frequently qualitative researchers make generalizable claims in their published work. We sampled articles ( N = 33) published from six psychological journals that regularly publish qualitative research and found that 100% of studies included claims related to statistical-probabilistic and analytic generalizability and that 85% related to transferability. A little more than half of studies explicitly described their epistemological framework. However, very few studies said that generalizability was not a goal of the study or noted a lack of generalizability as a limitation of the study. We then provide suggestions to better align qualitative claims with underlying assumptions. We argue that qualitative research can generalize but that qualitative researchers should be more transparent about aiming to do so.
A Practical Tutorial in R for Meta-Analytic Structural Equation Modeling With Hierarchical Effect-Size Dependency and Study-Level Moderators
Junhua Dang
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In this tutorial, I provide a comprehensive, step-by-step guide to conducting two-stage meta-analytic structural equation modeling with hierarchical effect-size dependency and moderation analyses. I illustrate the procedure using a data set of correlations between Big Five personality traits and psychological flow across multiple studies. The proposed workflow uses multilevel-multivariate random-effects pooling in Stage 1 and structural equation modeling in Stage 2. A key feature is a working-model approach to Stage 1 heterogeneity: I fit and compare interpretable heterogeneity structures (e.g., compound-symmetry and heterogeneous compound-symmetry variants) and select the best-fitting pooling model before constructing the Stage 2 inputs. I then demonstrate how to evaluate a categorical moderator (i.e., flow questionnaire) via multigroup comparison. I also illustrate how to test heterogeneity in individual path coefficients and conduct post hoc pairwise comparisons. By walking through the full two-stage meta-analytic structural-equation-modeling workflow using real data and reproducible R code, I intend this tutorial for researchers in psychology, education, and the social sciences who seek to synthesize findings across studies regardless of prior experience with meta-analytic structural equation modeling.
Addressing Missing Data From Prompt Noncompliance: A Practical Guide to Auxiliary Variables in Ecological Momentary Assessment
Stefan Schneider, Marta Walentynowicz, Meynard J. Toledo, Raymond Hernandez, Doerte U. Junghaenel, Joshua M. Smyth, Arthur A. Stone
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Missing data from noncompliance and missed prompts are pervasive in ecological-momentary-assessment (EMA) research. Although researchers often examine variables associated with prompt nonresponse, these variables are rarely incorporated into analytic models, leaving standard multilevel analyses to rely on missing-at-random (MAR) assumptions that are often implausible given the available data. In this tutorial, we introduce an accessible strategy for integrating such variables, known as “auxiliary variables,” directly into multilevel models to bolster the plausibility of the MAR assumption. We outline a practical framework for identifying promising auxiliary variables in EMA data sets (e.g., structural-design features, lagged self-reports, passive metadata) and provide step-by-step instructions for (a) preparing EMA data sets, (b) evaluating the utility of candidate auxiliary variables, and (c) incorporating them into multilevel models of EMA data. To facilitate adoption, we supply open-source R code for each step and introduce an R function ( EMAuxiliary ) that streamlines the estimation of multilevel models with auxiliary variables using freely available Blimp software. A fully worked example using real EMA data illustrates the approach. Our aim is to equip applied researchers with a practical, widely applicable method for reducing bias from missing prompts and strengthening inferences in EMA studies.
Using Nonbinding Experiments to Estimate Causal Effects With Instrumental-Variables Regression: A Tutorial
Sanford R. Student
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Often, causal effects of interest in psychological research involve causes that cannot be randomly assigned. Instrumental-variables (IV) regression, a cornerstone of the econometric literature on causal inference with nonrandom causes, allows for unbiased causal inference in the face of several threats to the validity of causal inference using ordinary-least-squares regression. IV regression is enabled by an instrument—a third variable that affects the outcome only through its influence on the hypothesized cause. One way to study causal effects in psychology is thus the random assignment of an intervention that drives change in the cause of interest but is otherwise unrelated to the outcome, followed by IV analysis to produce a valid causal effect. Moreover, IV regression models can be estimated straightforwardly as structural equation models (SEMs). Based on open data from a recently published study in the economics literature, in this tutorial, I outline the use of SEMs to estimate the causal effect of a behavior of interest, exercise, on an outcome of interest, academic performance, enabled by an instrument—a randomly distributed free gym card—that constitutes a random, nonbinding manipulation of exercise. The role of covariates in IV regression is outlined, and R code is provided for all analyses.

Behavior Research Methods

The MultiSOCIAL Toolbox: An open-source toolkit for advancing multimodal interaction research
Veronica Romero, Tahiya Chowdhury, Alexandra Paxton, Muneeb Nafees
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Human interaction is intrinsically multimodal, in that it unfolds across different kinds of behaviors (or modalities ). However, many researchers have found it difficult to systematically investigate the richness of multimodal communication due to its theoretical and technical complexity. To help overcome some of the technical hurdles, we introduce an open-source single-platform solution: the MultiSOCIAL ( Multi modal time S eries O pen-Sour C e I nteraction A nalysis L ibrary) Toolbox. The Toolbox enables any researcher who has video files to extract time-series data in three modalities: body movement to quantify non-verbal behavior through a pose estimation algorithm; transcripts of what was said during an interaction through the use of an automatic speech recognition system; and acoustic prosodic characteristics of speech through the use of audio signal processing. The toolkit uses existing gold-standard open-source tools, and we have brought them together in a freely available graphical user interface (GUI) that requires no coding and runs exclusively on the user’s machine. To provide a concrete use-case, we present an analysis of a new dataset collected by undergraduate students as part of a seminar class. Here, we specifically analyze the movement data from the study to exemplify the complexity available in video data from just one modality. We find that friends show higher interpersonal bodily coordination than strangers and that this coordination is both more stable and more complex throughout a simple affiliative conversation. We end with some practical recommendations for using the Toolbox, including how to best set up experiments to get the most accurate data possible to maximize data quality.
A systematic scoping review identifying effective virtual reality stress tasks inducing physiological reactivity for future wearables validation
Magdalena Sikora, Xiaochang Zhao, Jan-Willem van ’t Klooster, Zeynep Koyuncu, Eco de Geus, Matthijs Noordzij
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Efficient and ecologically sound validation of wearables remains a major challenge in stress research. Virtual reality (VR) could support this by providing standardized, immersive stress induction, but the field lacks a comprehensive and exhaustive overview of which VR tasks reliably evoke physiological stress responses. This review delivers a systematic comparison of VR stressors, mapping 11 tasks into three core stressor categories (social-evaluative, environmental, and cognitive) and offers a synthesis of their effectiveness, methodological characteristics, and suitability for VR-based wearables validation. We examined VR’s effectiveness for physiological stress elicitation, both qualitatively with the focus on task design and quantitatively through available effect sizes of responses. We observed high heterogeneity in stressor design and measurement methods, with the VR Trier Social Stress Test (VR-TSST) and the high-altitude task emerging as most promising VR stressors. Although several studies incorporated wearables, proving feasibility of such setups, none used VR explicitly for validating them. This review supports the use of VR for stress elicitation and for validation of wearable devices. It also enables informed stressor selection and VR study design while highlighting the existing gaps and future directions.
Disentangling substantive and method variance in mixed-worded scales: An empirical application of the random intercept item factor analysis (RIIFA)
Palmira Faraci, Giuliana Nasonte
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Measuring the dynamics of attentional capture in hand and eye movements with virtual reality
Max D. Gurr, Jeff Moher, Matthew D. Egbert, Christopher D. Erb
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Laboratory investigations of attentional capture typically use two-dimensional displays and button-press responses, which may limit their generalizability to everyday experiences. The current study used a virtual reality (VR) system to measure the dynamics of attentional capture by simultaneously recording participants’ ( N = 54) hand and eye movements as they completed a modified additional singleton task. In both a sparsely detailed and richly detailed virtual environment, participants reached to select a uniquely shaped target that was accompanied by a uniquely colored distractor on 50% of trials. We observed robust attentional capture costs across our measures of hand and eye movements, with greater latencies in initiation and movement times, larger reach curvatures, more overall eye movement, and delayed first target fixations on distractor-present trials. Three-dimensional target attraction and distractor attraction scores provided sensitive measures of the time course of attentional capture and allowed for more direct interpretations of performance than reach curvature. Although initiation times, response times, gaze distances, and target fixation proportions were elevated in the richly detailed environment relative to the sparsely detailed environment, we failed to observe a significant interaction between environmental detail and attentional capture costs in any of our measures. Taken together, our results demonstrate the feasibility of using VR to examine the dynamics of attentional capture in immersive virtual environments with richer, more continuous behavioral measures.
Creating and validating photorealistic AI-generated facial expression stimuli for emotion research
Shlomo Hareli, Shlomo David
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The development of AI-generated facial expression stimuli offers researchers unprecedented control over stimulus characteristics while maintaining photorealistic quality. However, rigorous validation methodologies are essential to ensure these stimuli function effectively in behavioral research. This paper presents a comprehensive three-study validation framework for AI-generated facial expression stimuli that systematically varied body weight and emotional expressions. Study 1 validated 72 stimuli across three emotions (happiness, sadness, neutrality) and three weight categories using both computational and human validation approaches. Study 2 extended this framework to include anger as a fourth emotion while controlling for individual facial features through same-character methodology. Study 2b further assessed valence, intensity, naturalness, authenticity, and non-focal emotion ratings of the same stimuli from Study 2. Across studies, the stimuli demonstrated successful emotion and weight manipulations, with AI-generated images achieving photorealistic quality that participants could not reliably distinguish from real photographs. Computational validation showed high intended-emotion likelihood in Py-Feat, while human validation confirmed appropriate perception of the target emotions, body weight, realism, age, and broader expression-quality dimensions. This dual validation approach—combining automated computational assessment with human behavioral validation—provides a robust framework for ensuring AI-generated stimuli meet research standards. The methodology offers significant advantages for behavioral research, enabling precise control over stimulus characteristics while maintaining ecological validity and addressing ethical considerations in stimulus development.
BriDGE the gap: Improving behavioral research by integrating DAGs and GAMs into experiments
Giuseppe Alessandro Veltri, Sanchayan Banerjee
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Understanding the mechanisms through which behavioral interventions work remains a critical challenge in behavioral science. While randomized controlled trials (RCTs) provide reliable evidence for intervention efficacy, they are seldom designed to reveal the underlying causal pathways that drive observed outcomes. We introduce a comprehensive data-driven methodological protocol – BriDGE – that combines advanced causal inference techniques, such as directed acyclic graphs (DAGs), causal discovery algorithms, and generalized additive models (GAMs), to enhance mechanistic insights in behavioral applications. BriDGE modifies conventional experimental analysis with a stepwise approach including DAG-based hypothesis formulation, modeling of nonlinear relationships with GAMs, and detailed mediation analysis. Using bootstrapping and sensitivity checks, BriDGE ensures robust and reliable detection of both direct and indirect effects. We use a simulation study to validate BriDGE’s ability to identify complex causal mechanisms, offering researchers a robust framework for deepening understanding of causal mechanisms and optimizing intervention design. To support adoption, we additionally provide practical guidance on mediator dimensionality, computational feasibility, and simulation-based power planning, including benchmarking templates implemented in the accompanying code. There are natural limitations of BriDGE – we discuss their implications when applied to public policy. We call for a greater integration of these methods in the toolkit of applied policy analysis to bridge the gap from “what works” to “why and how it works”. We also release BriDGE, an open-source R package that implements the workflow to facilitate adoption and reproducibility.
The effect of incentive type on data quality and quantity in an experience sampling study in a student population
Milla PihlajamÀki, Ginette Lafit, Olivia J. Kirtley, Inez Myin-Germeys, Gudrun Eisele
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The English Pseudoword Lexicon Project (EPLeP): A resource for investigating pseudoword processing
Fabio Marson, Rolando Bonandrini, Iva Ć aban, Simona Amenta, Marco Marelli
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In recent years, psycholinguistics has seen an increase in research focused on the processing of written stimuli that are not attested in the lexicon of a given language. We introduce the English Pseudoword Lexicon Project (EPLeP), a new database of lexical decision data for English pseudowords, nonwords, and words. While large-scale behavioral data have been collected for lexical and non-lexical items in English and other languages, none of these datasets have specifically focused on pseudowords. The EPLeP database fills this gap by considering three types of English-like non-lexical stimuli: (1) morphologically simple pseudowords generated from existing English words; (2) morphologically complex pseudowords obtained by combining existing stems and existing suffixes; and (3) nonwords, not following English graphotactics. This registered report provides an extensive overview of the steps adopted in producing non-lexical stimuli and the criteria for their selection. We collected data for almost 13,500 non-lexical and 4,400 lexical stimuli from 1,416 English-speaking participants across four online studies. Analyses of behavioral responses show that morphologically complex pseudowords trigger the deepest level of processing, whereas nonwords elicit the shallowest processing, reflecting a crucial difference in how these stimuli affect responses to words. This database will be a valuable tool for researchers interested in both pseudoword and word processing, enabling them to evaluate hypotheses by analyzing the collected behavioral data or using them to select stimuli for future studies. The Stage 1 protocol was accepted in principle on July 4, 2025 ( https://osf.io/cz9rh/ )
CoGrid & the Multi-User Gymnasium: A framework for multi-agent experimentation
Chase McDonald, Cleotilde Gonzalez
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The increasing integration of artificial intelligence (AI) in everyday life brings with it new challenges and questions regarding how humans interact with autonomous agents. Multi-agent experiments, where humans and AI act together, can offer important opportunities to study social decision-making, but there is a lack of accessible tooling available to researchers to run such experiments. We introduce two tools designed to reduce these barriers. The first, CoGrid , is a multi-agent grid-based simulation library with dual NumPy and JAX backends. The second, Multi-User Gymnasium ( MUG ), translates such simulation environments directly into interactive web-based experiments. MUG supports interactions with arbitrary numbers of humans and AI, utilizing either server-authoritative or peer-to-peer networking with rollback netcode to account for latency. Together, these tools can enable researchers to deploy studies of human–AI interaction, facilitating inquiry into core questions of psychology, cognition, and decision-making, and their relationship to human–AI interaction. Both tools are open source and available to the broader research community. Documentation and source code is available at {}. This paper details the functionality of these tools and presents several case studies to illustrate their utility in human–AI multi-agent experimentation.
An LLM canary in the online data coalmine: Bayesian reasoning problems as a capability-gap test for LLM contamination in online samples
Vera Wilde
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Computers in Human Behavior

Trust begins at home: The role of psychological distance in trust transfer for emerging technology
Qingrui Li, Huimin Chen, Yujing Lin
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Aggregate convergence and constituent divergence in incel-related online communities: A mentalizing-theory lens on cross-platform linguistic patterns
Bochen Li, Cass Dykeman
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Gendering conversational AI in human–AI interaction: The role of interaction context and sexist ideology in shaping user evaluations
Ye Wang, Xuxu Liu, Yaling Deng, Hongjiang Xiao, Yuan Zhang
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Capitalizing on Positive Experiences: Receiving Affective Empathy Fosters a Better Mood and Prosocial Waiting
Mingping Li, Yuwen Yang, Xiaodi Wang, Yanjie Su
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A Longitudinal Study of Patterns of Direct Online Harms Victimization in Six EU Countries
Sandy Schumann, Magdalena Celuch, Emmi Kauppila, Iina Savolainen, Atte Oksanen
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Incompetence or Deception: Unraveling the Asymmetric Mechanisms of AI Hallucinations on Trust and Distrust
Kun WANG, Zhao PAN, Yaobin LU
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Personalizing AI Voice Agents: How Voice Agents with Similar Voices Shape Trust, Self-Congruency, and Behavior
Yun Fu, Martin Meißner
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Beyond tasks: Environment and interaction as active components in virtual reality interventions for dementia
Pai Liu, Daiyue Lu, Jessica Fernandez
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Comparing AI chatbot users and non-users in Germany: Reasons for use, addictive AI chatbot use, mental health, and loneliness
Julia Brailovskaia, Lena-Marie Precht, Falk Gerrik Verhees, Pauline Schmidt, JĂŒrgen Margraf
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Newcomers help most after being helped, veterans do not: Evidence from Stack Overflow
Lenard Strahringer, Sven Eric Pruess, Kai Riemer
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Divergent Relationships Between Moral Disengagement and Cyberbullying Bystander Response Behaviors: Examining the Amplifying Roles of Humor and Anonymity
Zirui CHEN, Clifton R. EMERY
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Who Falls for the Bait? Deciphering the Psychological Architecture and Network Dynamics of Vulnerable Youth
Feng Sun, Jun Gao, Haichao Zhao, Ofir Turel, Qinghua He
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Group Processes & Intergroup Relations

Adolescents’ Future Marriage Preferences: The Codevelopment of National Identity, Attitudes Towards Immigration and Intergroup Marriage Preferences Across Time
Alexander W. O’Donnell, Morgana Lizzio-Wilson, Michael Thai, Emma F. Thomas, Fiona Kate Barlow
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Human development involves constructing an imagined future, with young people often envisioning their future spouse and family composition. The extent to which national, religious and racial preferences emerge during this time is largely unknown. Using a large sample of Australian-born, English-speaking adolescents, the relationships between national identity, attitudes towards immigration and ingroup marriage preferences were assessed into young adulthood. Random intercept cross-lagged panel models revealed significant within-person effects, whereby higher national identity indirectly predicted stronger national and racial marriage preferences 5 years later via increasingly critical attitudes towards immigration. Further, stronger national and racial marriage preferences predicted future increases in negative attitudes towards immigration, which in turn was associated with increased national identity several years later. Although marrying someone from the same religion was reported by young people to be important to them, national identity was not associated with changes in religious marriage preferences. These longitudinal effects suggest that simultaneous efforts to promote a more inclusive national identity and normalise intergroup intimacy could reduce prejudices during a formative developmental period.
The Fight for the Climate: Social Identities, Grievances, and Authoritarianism Drive Support for Climate Activism and Radicalism
Oluf GĂžtzsche-Astrup
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The combination of increased political authoritarianism and an accelerating climate emergency has led to worries of an uptick in political violence among activists. This paper takes a social psychological approach to develop hypotheses around the role of social identities, personal grievances, and authoritarianism in driving support for more radical climate activism. It tests these hypotheses in a conjoint experiment across the US, the UK, and Denmark (total N = 2,447). The experiment varies characteristics of activists and asks respondents to indicate their support for and willingness to join conventional and transgressive protest groups. Results show that identification with the climate movement and left-wing political alignment constitute protective factors against, rather than accelerators of, radical action. Rather, left-wing authoritarianism emerges as a core driver of more radical responses. The more left-wing authoritarian the respondent, the more open to sympathizing with and willing to join radical protest groups. The paper contributes to our causal understanding of group processes involved in protest and reintroduces conjoint experiments to this domain.

Journal of Experimental Social Psychology

Exposure to rising societal economic inequality reduces the misperception of racial economic equality
Sa-kiera T.J. Hudson, Julian M. Rucker, Bennett Callaghan, Michael W. Kraus, Leslie McCall, Jennifer A. Richeson
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Living beyond duty: People see meaning and happiness in supererogation
Ishita Singhal, Fan Yang
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Personality and Social Psychology Bulletin

On Confederate Monuments, Their Removal and Racial Bias
Maximilian A. Primbs, Nicolas Sommet, Gijsbert Bijlstra, B. Keith Payne, Ruddy Faure, Tessa A. M. Lansu, Rob W. Holland, Amanda B. Diekman, Heidi A. Vuletich
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The presence of Confederate monuments and their removal has been a frequent topic of societal discussion in the United States. Taking a situational perspective (i.e. the Bias of Crowds model), the present paper investigated whether the presence of Confederate monuments and their removal from an area causally affect the racial biases of people in that area. Across multiple large-scale data analyses ( N 1 = 1,753,557), online experiments ( N 2a = 1,164, N 2b = 416, N 3 = 537), and a field study ( N 4 = 176), we found no evidence that the presence or removal of Confederate monuments reliably affects the implicit (or explicit) racial biases of people. We discuss implications for situational models of bias and best research practices for conducting geographical difference studies.
Is Authenticity Perceptible? Accuracy in Judgments of State Authenticity From Expressive Behavior
Vanessa K. Castro, Serena Chen, Joseph M. Ocampo, Amie M. Gordon, Emily A. Impett, Max Weisbuch
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Substantial research provides evidence that being perceived as authentic benefits well-being and relationships, but under-examined is whether perceptions of authenticity are accurate. Across five studies, we tested whether perceivers can accurately discern others’ state authenticity. In Studies 1 to 3, we investigated correspondence between perceived and felt authenticity within acquainted dyads. Romantic partners accurately discerned each other’s authenticity after making sacrifices (Study 1) and after discussing a topic of personal distress and desired partner change (Study 2). However, partners failed to accurately perceive each other’s felt authenticity when discussing relationship conflict (Study 3), possibly impacted by cognitive or motivational processes elicited by the relatively high interpersonal stakes at play in conflict. In Studies 4A and 4B, outside observers were sensitive to authenticity differences in participants from Study 3 when watching thin-slice videos of the conflict conversations. Overall, we discuss the importance of authenticity for impression formation and close relationships.

Psychological Methods

The denominator chooses the estimand: A target-population true-score framework for standardized mean differences.
Daiki Nakamura
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Transitive differential item functioning clustering: A graph-theoretic approach to identifying many-group partial measurement invariance.
Yale Quan, Chun Wang
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Identifying causes of treatment effect heterogeneity across replication studies: Definitions, identification, and estimation of remaining heterogeneity.
Soojin Park, Steffi Pohl, Peter M. Steiner
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How to run round-robin social interaction studies: A tutorial for the efficient capture of natural social behavior.
Erin A. Heerey, Samantha M. Jones, Jeremias Campos, Amanda Friesen
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Psychological Science

Better Together? Facial Expressions as a Mechanism Shaping Enjoyment During Shared Experience
Argaman Bell Meir, Liron Amihai, Daniel Toledano, Inbal Ravreby, Yaara Yeshurun
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We explored the impact of shared experiences on enjoyment, focusing on the role of facial expressions. Participants ( N = 190, aged 18–35, recruited in Israel) listened to humorous audio clips with a friend or alone, while their facial expressions were recorded via Zoom. Our primary analysis showed that friend presence influenced enjoyment in a content-dependent way, enhancing enjoyment for knock-knock jokes and reducing it for stand-up. Across both types of humor, friend presence amplified the intensity of happy facial expressions. Complementing these findings, a post hoc mediation analysis revealed that these happy expressions partially accounted for shared experiences’ effect on enjoyment, suggesting they are a key mechanism linking social context to affective responses. Friends also exhibited facial-expression synchronization, reflecting communicative responsiveness to one another. Notably, these effects were reduced when participants could not see each other. These findings suggest that facial expressions actively shape enjoyment during shared experiences, offering insight into how people connect and experience the world together.
Effects of a Theory-Based Smartphone Intervention to Decrease Neuroticism in the General Public
Amanda J. Wright, Peter Haehner, Rosalie Andrae, Till Lubczyk, Christopher J. Hopwood, Wiebke Bleidorn
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Many people wish to be less neurotic, and such decreases could have far-reaching benefits, but evidence on nonclinical interventions and change mechanisms is limited. Using a German-speaking, adult sample ( N = 446), we conducted a 6-week, smartphone-based randomized controlled intervention to test effects of theory-based strategies targeting different neuroticism levels and aspects. Across groups, there were significant self-report effects ( d effect = −0.22 to −0.37) that persisted through a 2-month follow-up ( d effect = −0.16 to −0.33), and follow-up peer-report effects for one of two measures ( d effect = −0.18 to −0.30). Treatment-group comparisons indicated that state-level strategies fostered larger immediate (albeit shorter-lasting) effects, whereas habit-level strategies led to smaller initial declines that strengthened over time. Behavior-focused strategies, for states and especially habits, yielded particularly strong effects across assessment methods, both independently and incrementally. Our findings demonstrate that neuroticism can be changed within 6 weeks and provide insight into effective mechanisms of change.
Disillusionment With Meritocracy During the Transition to Adulthood: Longitudinal Change in Belief Systems and Depression in China
Shuming Fan, Oliver P. John, Filip De Fruyt
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Meritocracy —the belief that individual effort rather than structural advantages drives success—has long shaped societal narratives but now faces growing skepticism. Using four waves of China Family Panel Studies, we examined changes in meritocratic and nonmeritocratic beliefs among 524 participants from early adolescence (ages 10–13 in 2012) to emerging adulthood (ages 18–21 in 2020) and assessed their concurrent associations with depression trajectories. Meritocratic beliefs declined significantly in both college ( d = −0.56) and noncollege ( d = −0.30) groups. Only college students increased in nonmeritocratic beliefs ( d = 0.72), and these changes were associated with steeper increases in depression. College students’ recognition of structural barriers to success is associated with heightened psychological costs, although causality cannot be inferred.

Psychology of Popular Media

A message from your outgoing editor.
Karen E. Shackleford
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Differential effects of social media on body esteem: An ecological momentary assessment on weight satisfaction and muscularity importance.
Megan Van Alfen, Sarah M. Coyne, Joseph A. Olsen, Drew P. Cingel, Adam A. Rogers, Ashley Larsen Gibby
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Eye tracking and the self-view: Self-focused attention on Zoom is a limited predictor of appearance-related concerns.
Amelia C. Couture Bue, Julie Aprill, Jacob Russell, Mariia Pugina, Carrie Grim, Jessica Simmons, Mania Mohseni, Sheena Hunter
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Editors’ introduction to the special issue: New media and body image.
Megan A. Vendemia, Teresa Lynch
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Technology, Mind, and Behavior

Digital intimacy: Motivations, perceptions, and perceived growth in human–artificial intelligence companionships.
Zeena Whayeb, Robert Hymes, Michelle Beddow
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Nature in virtual reality and curiosity: The mediating role of restorativeness.
Davide Clemente, Claudia Russo, Luciano Romano, Annalisa Theodorou, Elena Rinallo, Camilla Romano, Stepan Vesely, Angelo Panno
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