I checked 15 psychology journals on Wednesday, September 23, 2026 using the Crossref API. For the period September 16 to September 22, I found 30 new paper(s) in 10 journal(s).

Advances in Methods and Practices in Psychological Science

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.
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.
This Is the Reason for External Replication: Response to Aungle et al.
Nicholas J. L. Brown, Andrew Gelman
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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.
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.
The English Pseudoword Lexicon Project (EPLeP): A resource for investigating pseudoword processing
Fabio Marson, Rolando Bonandrini, Iva Ĺ 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.

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 Cross-Platform Linguistic Analysis
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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Platform abandonment through push–pull–mooring: Trust, commitment, and generational patterns in U.S. social media
Yee Man Margaret Ng, Ewa Maslowska
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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, JĂĽrgen Margraf
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Organizational responsibility attribution: Public responses to AI versus human failures in government services
Di Zhou, Zepeng Gong
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Nodding of virtual humans does not necessarily increase customer trust: a temporal analysis
Joris R.H. van der Harta, Michelle M.E. van Pinxterena, Roel Boumansa, Tibor Bossea
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Multimodal Cognitive and Neurophysiological Differences Between Esports Players, Traditional Athletes, and the Gamer Population: A Comparative Study Using qEEG, Cognitive Testing, and the BARD Competency Framework
Oliver Szabella, Laszlo Szaloki, Daniel Samuel Kutyej, Attila Varhegyi, Barbara Berta, Zoltan Borbas, Laszlo Csakvari, Daniel Bella, Marton Kekesi, Gergely Hajdu, Mira Ambrus, Balazs Biro, Dorina Annar, Irina Kalabiska
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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

Stay Healthy for Your (Our) Community: Examining How Source Identity Affects Black Americans’ Responses to Targeted COVID-19 Messages
Evava S. Pietri, Veronica Derricks, India R. Johnson, Sheba M. Aikawa
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Many communications during the pandemic emphasized COVID-19’s disproportionate impact on Black Americans. Articles highlighting such racial health disparities can act as a form of health information targeting , which can be threatening when aimed at marginalized identities. In Study 1, Black participants indicated a greater level of anger and distrust when reading a targeted article from a White male doctor, focusing on racial health disparities during the pandemic, compared to a non-targeted article. Source distrust was associated with decreased message persuasiveness and intentions to engage in safety behaviors. Moreover, a Black male doctor was generally more persuasive, mitigating the adverse effects of message targeting on source trust. Replicating these findings in Study 2, a Black male doctor/source was more persuasive than a White female doctor among Black women participants. Interestingly, perceptions of source bias and expertise showed a different pattern than trust, demonstrating the distinct link between distrust, targeted messaging, and threat.
Causal Evidence for the Role of Respect Norms in Promoting Tolerance
Lucía Estevan-Reina, Marcin Bukowski, Laura Frederica Schäfer, Anna Potoczek, Dominika Gurbisz, Oliver Christ
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Promoting social norms can be a powerful tool to deal with the intolerance that threatens our societies. This research explores how equality-based respect norms foster intergroup tolerance. We developed an experimental paradigm manipulating equality-based respect norms within social media to promote tolerance toward the LGBTIQ+ community in Poland. Across three studies (Study 1: N = 98; Study 2: N = 381; Study 3: N = 377), manipulations consistently affected perceptions of descriptive and injunctive norms in a Facebook post and in Polish society. Moderation analyses showed that the effect of our manipulation on tolerance emerged only among participants with less positive attitudes toward homosexual people (Study 1) or stronger disapproval of same-sex marriage (Study 3). This supports the conceptualization of tolerance as respecting others despite disapproving of their behavior. We discuss the implications of this research for the promotion of respect norms and tolerance as a way to deal with the challenges associated with divisions in society.
Open Borders, Restrained Change: How Group Permeability Shapes Intergroup Evaluations and Social Hierarchy
Julien Barbedor, Jennifer Bastart, Laurent Cambon, Vincent Yzerbyt
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Building on Social Identity Theory (SIT), the dimensional compensation model (DCM) suggests that people rely on the Big Two dimensions of social evaluation to manage their group identity: members of low (high) status groups compensate their relative lower (higher) standing competence by stressing their higher (lower) level on warmth. Because SIT highlights permeability as an important structural variable that shapes people’s identity management strategies, we hypothesized that stronger permeability should induce less compensation, especially on warmth. In Studies 1a, 1b, and 2 (total N = 1,694), we measured permeability using a social ladder paradigm. In Study 3 ( N = 2,263), we manipulated permeability by relying on a fictitious company scenario. Our results confirm that judges were less inclined to compensate when permeability was high rather than low. These findings support the idea that permeability is an important structural variable that shapes the compensation effect for both high- and low-status perceivers.

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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Journal of Personality and Social Psychology

Dynamic processes in narcissism and affect across Chinese and American individuals: A registered report.
Yuping Liu, Bingtao Zhou, Leon P. Wendt, Elizabeth A. Edershile, Bo Yang, Aidan G. C. Wright, Johannes Zimmermann, Christopher J. Hopwood
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When and why are social categories overused relative to individuating information? A Bayesian approach to identifying biases in impression formation processes.
Thalia H. Vrantsidis, William A. Cunningham
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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.
Approach–Avoidance Tendencies Across Societies: How Mobility and Competition Shape Human Motivation
Kuan-Ju Huang, Yen-Ling Chen, Yan Li, Xian Zhao
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Approach and avoidance are two foundational systems that drive human behavior. Although these systems are generally considered to be shaped by neurobiological factors, approach and avoidance tendencies vary systematically across societies. However, there is currently no framework that explains these cross-societal variations. We propose a socioecological framework to explain how environments with varying levels of relational mobility and societal competition shape individuals’ sensitivity toward reward (i.e., approach) or punishment (i.e., avoidance). Using five studies involving individuals from China, Japan, Taiwan, and the United States ( N = 3,639), we found a complex pattern of approach and avoidance tendencies across societies. Specifically, Japanese participants are more avoidance-oriented than U.S. participants, while Chinese and Taiwanese participants are as or more approach-oriented than their U.S. counterparts. These variations were associated with the ecologies of relational mobility and societal competition. Together, these findings offer a novel mobility–competition framework for understanding societal differences in human motivation.

Psychological Methods

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.

Technology, Mind, and Behavior

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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