I checked 15 psychology journals on Thursday, November 12, 2026 using the Crossref API. For the period November 05 to November 11, I found 26 new paper(s) in 10 journal(s).

Advances in Methods and Practices in Psychological Science

Mind the Gap: A Practical Guide for Effectively Addressing Missing Data in Moderation Analysis
Guyin Zhang, Amanda J. Fairchild, Brian Keller, Dexin Shi
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Advances in methodology and statistical software have made modern techniques for handling missing data more accessible and easier to implement. However, optimally applying these methods in moderation analysis remains challenging given assumption violations that arise from the nonnormality of interaction term when traditional approaches are used and nuances faced when modeling interaction terms in the presence of item-level missing data that must be reconciled with scale-level analyses. To address these issues, we present a comprehensive and practical user’s guide on implementing an advanced approach, factored regression specification (FRS), that ideally handles missing data in observed-variable-moderation models. Specifically, we provide step-by-step guidance for conducting FRS at both scale and item levels in various interaction scenarios that include missing data with both continuous and categorical variables. We also derive and present a new coding method to address the challenge of probing simple slopes in continuous-variable interactions that contain item-level missingness. Finally, we supply R code to facilitate implementation of the methods described and offer practical recommendations for researchers.
Automating Abstract Screening in Research Synthesis Using Large Language Models: A Tutorial and Proof-of-Concept Study
Mirka Henninger, Jan Radek, Jean-Paul Snijder-Steinhilber, Martin Pauly
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Screening abstracts is a crucial yet labor-intensive step in research-synthesis projects, such as systematic reviews and meta-analyses. Large language models (LLMs) promise an opportunity to streamline and automate this process. However, there is still little guidance and practical insight into how such automated workflows can be implemented in research practice. In this article, we propose, describe, and illustrate an abstract-screening procedure using LLMs including a quantification of uncertainty of model outputs. We provide a step-by-step tutorial in which we address how to select an LLM, access LLMs from within R, develop and refine suitable prompts, define structured output formats, and describe whether and how model hyperparameters should be set. We then illustrate this procedure in a proof-of-concept study using a set of human-rated abstracts from a recent meta-analysis for comparison. We demonstrate that LLM-assisted screening can substantially reduce the time and cost of review preparation while maintaining accuracy comparable with human raters. To support psychological researchers in adopting LLM-based workflows for research synthesis, we also provide R scripts and implementation guidance on Gitlab. At the same time, we emphasize that this work represents an initial step and that continued refinement and validation are essential given that LLM technologies and their applications continue to evolve rapidly.
How to Play With Ideas Using Computer Simulations: A Gentle Introduction to Computational Modeling via Systems Dynamics
Christopher R. Dishop
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Computational modeling provides a way to translate theory into explicit representations that can be simulated and examined, but many researchers lack a clear entry point for doing so. In this article, I introduce a two-step procedure designed especially for psychologists, particularly psychologists with little or no programming experience, who are interested in exploring computational modeling but are unsure how to start. The procedure involves two steps: (a) converting box-and-arrow diagrams (which many psychologists are already familiar with) into stock-and-flow (SF) diagrams and then (b) developing pseudocode from the SF diagram. Once these steps are completed, translating the model into executable syntax becomes straightforward. The goal is not to teach programming or simulation in depth but to show how conceptual models can be expressed in a form precise enough to generate trajectories over time. I then describe why this translation matters for theory. Explicit computational representations allow researchers to compare alternative explanations, evaluate whether simpler mechanisms are sufficient to reproduce observed patterns, examine whether theories generate the outcomes they claim, and explore ideas or policies that would be difficult to test directly in the field. Ready-to-use R code is provided to support hands-on learning to reach the broader aim of making computational modeling a practical and approachable tool for psychologists interested in evaluating their theories with computational modeling.

Behavior Research Methods

The Lyric Focus Questionnaire (LFQ): Measuring individual differences in song listening
Dominique T. Vuvan, Julia Cannistraro, Wren R. R. Goodkind, Megha Sharda
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CzeLex: A megastudy dataset of concreteness, imageability, age of acquisition, valence, and gender ratings for 9601 Czech words
James Brand, Filip Smolík, Jan Chromý, Markéta Ceháková, Mikuláš Preininger
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Understanding how semantics are processed at the lexical level has been a key aim for the cognitive sciences, with large-scale semantic rating studies in different languages playing a crucial role in facilitating the advancement and development of theories of lexical processing, as well as providing valuable methodological resources that can be used in empirical investigations. In this paper, we present the CzeLex database, a megastudy of 9601 Czech words that have been normed along five psycholinguistic dimensions from a large sample of 4405 Czech-speaking participants. We provide individual rating data at the item and participant levels, as well as aggregated data from both mean ratings and latent means derived using cumulative-link mixed modeling, along with a comprehensive analysis comparing aggregation methods. We demonstrate that the dataset has good reliability and that specific dimensions predict lexical processing measures in a lexical decision task, regardless of the aggregation method used. These norms are the largest available subjective rating resource for Czech and further contribute to the growing body of languages with openly available data on these dimensions. We hope that the dataset will be a valuable resource for researchers working on Czech, in addition to opening up new opportunities for cross-linguistic semantic investigations. All data, code, and analyses are available at https://osf.io/tqxfm .
Spelling Profile Assessment (SPA): A new spelling test for English primary school children; standardization
Georgia Niolaki, Harry Purser, Aris Terzopoulos, Janet Vousden, Saskia Kohnen, Jackie Masterson
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We present the construction, psychometric evaluation, and administration of a new single-word spelling battery that includes: a. irregular (e.g., yacht) and regular (e.g., trap) words and pseudowords (e.g., knep) ( N = 87), and b. a morphological spelling test with real words (20 affixes/seven suffixation rules) and pseudowords (mirroring the rules of the real words) ( N = 40). After the pilot phase, the final battery was standardized on 1053 children (Year_R to Year_6 F = 514). The battery across tasks presents outstanding reliability (.97 to .99), and criterion and convergent validity were compared with the Wechsler Individual Achievement Test-Third UK Edition (Wechsler, 2009) spelling subtest. The Spelling Profile Assessment (SPA) battery provides standard scores (mean = 100, Sd = 15) per subtask, a total composite standard score for each test (a and b) and 95% confidence intervals. We calculated age-based standard scores using a continuous developmental trajectory rather than assigning individuals to fixed age groups. We model gradual developmental change to provide a more precise score. The test also includes a stopping rule, which streamlines its administration and reduces stress for children who can only spell a limited number of items. Additionally, we automated every step of the standardization process and made it available under open access, ensuring transparency, reproducibility, and ease of adaptation. This tool contributes to the spelling literature by offering a comprehensive battery for assessing phonological, orthographic, and morphological spelling skills in primary-age children. It can serve as a valuable tool for educators/clinicians to gauge students’ progression and tailor their spelling instruction accordingly.

Computers in Human Behavior

From Augmented Self to Augmented Reality: Perceived Self-Reference and Its Consequences for Brand Responses
Anja Lambrecht, Carsten Baumgarth, Jörg Henseler
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Remembering the Unreal: The memory cost of deepfake faces
Alessandro Santirocchi, Pietro Spataro, Neil W. Mulligan, Aicia Naser, Roberto Atzeni, Antonino Esposito, Vincenzo Cestari, Clelia Rossi-Arnaud
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Between Financial, Digital, and Graph Literacy and Fraud Victimization: Disentangling Components and Cognitive Biases
Marco Barone, Candida Bussoli, Lucrezia Fattobene
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Online Racial Discrimination on Social Media and Social Anxiety Among Black Emerging Adults: Examining the Indirect Role of Racism Hypervigilance
Henry A. Willis, Antoinette Dorzon, Kamryn McKenzie, Lena-Sementa Weldemicael, Tida Tambedou
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Online, offline, and hybrid identity: Exploring domain-based social identities and their implications for technology attitudes, wellbeing, and social cohesion
W.J. Bingley, K.J. Reynolds
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‘Race Talk’ in Immersive Virtual Environments: Examining White Americans’ Responses to Racial Violence During an Interracial Interaction
Valerie Jones Taylor, Sandra Manfreda, Juan V. Valladares, Jared Cutler
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Designing human-centered generative AI for older adults: Understanding personalization and credibility across interaction contexts
Mingxi Sun, Jingxi Chen
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The role of haptic feedback in simulated social VR for older adults: Affective experiences, social interaction, and design implications
Dezijian Zhou, Anastasia Globa
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Group Processes & Intergroup Relations

The Dual-Process Motivational Perspective on Anti-American Attitudes: Evidence from Russia
Kirill Zhirkov, Eduard Ponarin, Sharon Werning Rivera, Shane Hsuan-Yu Lin
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In this paper, we explore the psychological motivations of anti-American attitudes from the perspective of the dual-process model that emphasizes two basic human motivational goals: security (with the resulting perception of threat) and superiority (with the perception of competition). Among established measures in social psychology, the former most closely corresponds to authoritarianism, whereas the latter is captured by social dominance orientation (SDO). We analyze the case of Russia where grievances against the United States featured prominently in the justification of the 2022 invasion of Ukraine. In Study 1, we find that political and cultural grievances against the United States, as well as anti-American attitudes and support for confrontation, are more strongly and consistently predicted by authoritarianism than by SDO. In Study 2, we replicate this result and also show that anti-American attitudes in the Russian public are motivated by perception of threat rather than perception of competition.

Journal of Experimental Social Psychology

A reanalysis of eye-tracking evidence for the diagnostic features account
Brandon Cohen, Debbie S. Ma, Tomás A. Palma, Eric M. Elias, Balbir Singh, Joshua Correll
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When racial violence becomes personal: how racial mega-threats transfer across racially stigmatized groups
Angelica Leigh, Tosen Nwadei, Marla L. White, Victor L. Marsh, Brian Lattimore
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Journal of Personality and Social Psychology

The more, the better? Evidence of parallel growth in intergroup contact quantity and quality over time.
Rose Meleady, Patrick F. Kotzur, Deborah Shulman, Gordon Hodson, Richard J. Crisp, Danielle Blaylock, Joanne Hughes, Maarten H. W. Van Zalk, Katharina Schmid
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Individual, interpersonal, and socioeconomic predictors of divorce: A 12-year prospective study.
Molly G. Smith, Jared M. Block, Thomas N. Bradbury, Benjamin R. Karney
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Personality and Social Psychology Bulletin

Increasing Support for Humanitarian Aid to Palestinian Civilians During the Israel–Gaza War Through Ideologically Tailored Social-Media Campaigns
Nimrod Nir, Eran Halperin
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Promoting support for humanitarian aid to an adversarial outgroup is difficult during active war. We introduce a digital field-experimental approach that embeds psychologically grounded interventions in people’s actual social-media environments. During the Israel–Gaza war, 6,105 adult Jewish Israeli Facebook users were assigned to a no-study-campaign control condition, a humanitarian-moral campaign emphasizing humanitarian obligation, or an instrumental-security campaign presenting aid as advancing Israeli security. Days after the campaigns ended, 4,784 participants completed the follow-up survey. Support for humanitarian aid declined in the control condition but increased under both campaigns, suggesting that psychological social-media interventions can buffer or reverse erosion of humanitarian attitudes during acute conflict. Political ideology moderated relative effectiveness: the instrumental-security campaign was especially effective among conservatives, whereas liberals responded positively to both. These findings provide partial support for ideological message matching and offer a scalable, ecologically valid framework for testing intergroup interventions in real-world settings.
Humanlike Self-Enhancement and Self-Protection in Generative Pre-Trained Transformer-Based AI Agents: Of Overclaiming and the Self-Serving Bias
Siwei Fu, Constantine Sedikides, Weihua Zhou, Xinyue Zhou
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We investigated whether generative pre-trained transformer (GPT)-based AI agents, despite presumably lacking humanlike sentience, emulate motivated behavior in group settings. We focused on overclaiming (overestimating one’s contribution to group outcomes) and the self-serving bias (SSB; taking disproportionate credit for group successes while deflecting blame for group failures). We conducted eight experiments involving a collaborative and creative group task (joke-writing). AI agents overclaimed their own contributions. An egocentrism-reduction manipulation (directing attention equally to one’s own and others’ contributions) attenuated, but did not eliminate, overclaiming. Further, AI agents exhibited the SSB. Overclaiming persisted across variations in temperature settings and demographic personas (gender, ethnicity) and was more pronounced in later GPT models. Also, chain-of-Thought reasoning did not reduce, and instead increased, overclaiming. AI systems can mimic human self-enhancement and self-protection, raising implications for the emergence of motivated self-like behavior in non-biological systems, the assumed neutrality of large language models, and the need for improved mitigation techniques.
Evolving Norms of Market Fairness: Stable Mechanisms and Shifting Standards in a Contemporary Replication of Kahneman et al. (1986)
William Rose, Joe J. Gladstone
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Do the fairness norms people use to judge economic life stay fixed or shift as markets change? We report four replications ( N = 1,016) of Kahneman et al.’s 1986 study of market fairness, using original and modernized scenarios in crowdworker and general-population samples. The cognitive machinery of fairness has barely changed in four decades: participants treated existing prices and wages as entitlements, judged pay cuts more harshly than equivalent withheld raises, and condemned firms exploiting market power. The fairness norms themselves, however, have shifted, and in different directions across market domains. Relative to 1986, the odds of objecting to price increases fell by roughly 40% to 55% in every sample, whereas only the younger crowdworker samples objected more to employer actions; the general-population panel showed no such shift. Fairness pairs stable mechanisms with standards that adapt to the economy, challenging theories that treat it as a single, fixed preference.
Adopting a Multidimensional Approach to Assessing Contextual Predictors of Regional Implicit Gender-Career Stereotyping
Jared M. Cutler, Kimberly Barsamian Kahn
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This study adopts a multidimensional approach to assess whether geographic areas with greater gender representation and integration (even distribution of employed men and women) have less traditional implicit gender-career stereotyping (IGS). Contact with and exposure to working women (counterstereotypic exemplars) may reduce regional IGS. However, this may depend on the type of career positions, as stereotypic (feminine careers) gender-career representation may exacerbate IGS. Researchers analyzed data from the U.S. Census’ 2010 to 2019 American Community Survey (metropolitan statistical areas: N = 338) and Project Implicit ( N = 689,455). Using multilevel modeling, representation and integration negatively predicted traditional IGS in that region. Furthermore, the proportion of working women in stereotypically masculine careers negatively predicted IGS, whereas employed women in feminine careers positively predicted IGS. Findings support environmental models of implicit associations and imply that enhancing the proportion of working women in masculine careers may be a method to addressing implicit gender bias.

Psychological Methods

Integrating artificial intelligence into back-translation: Opportunities, constraints, and recommendations for psychological research.
Julian Decius, Emily Kleszewski, Hannah Louisa Krüger
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Psychology of Music

Ratings of Musical Hooks in Unfamiliar Pop Music Are Influenced by Repetition and Distraction
Bethany T. Nixon, Sara C. Hamden, Timothy P. Byron
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Popular music’s widespread appeal has often been attributed to its use of ‘hooks’ – musical or lyrical phrases that are salient and easily remembered. This paper reports two experiments examining the role of attention and familiarity in shaping judgements of the salience and memorability of unfamiliar pop music. In Experiment 1, participants listened to unfamiliar pop songs either once or five times, while either attending to the music or completing sudoku puzzles, before using continuous self-report (CSR) to rate the salience and memorability of the full song. The results found that participants more familiar with the song after repeated listening rated it more highly, but that sudoku puzzles were not significantly distracting enough to affect ratings. Experiment 2 largely replicated Experiment 1, except with a verbal task as distraction, and involved providing post-listening ratings of song excerpts. Findings suggest that familiarity led to higher ratings of the excerpts, while attentional distraction led to lower ratings. Collectively, these results indicate that both attention and familiarity contribute to the cognitive processing of hooks in popular music. This research suggests that self-report ratings are an effective tool for psychologically investigating hooks and provide a foundation for future research on how they may influence the experience of earworms.

Technology, Mind, and Behavior

Coordinating actions and sharing control with a disembodied artificial intelligence: Task-dependent differences from human partners.
Noëlle Bender, Hendrik Damm, Nicole C. Krämer
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