I checked 15 psychology journals on Wednesday, November 11, 2026 using the Crossref API. For the period November 04 to November 10, I found 31 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 news we use: A systematic scoping review of fake news stimuli in psychological research
Theodore C. Webster, Ariana Modirrousta-Galian, Faye C. Skelton, Marina C. Wimmer
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Research into fake news has surged in popularity, with over 3,000 publications on the topic each year since 2021. Definitions of what constitutes “fake news” vary across the field of psychology, where source, number, content, and presentation of stimuli may all be different yet still described as “fake news.” Thus, conclusions drawn from fake news research may only generalize to the particular definition or presentation of fake news used in a given study. Relatedly, stimuli may not be available or described clearly enough for reproducibility. To assess the scope of these potential issues, we conducted and reported a PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) systematic scoping review of three databases including the last 11 years of psychology studies that used fake news stimuli. From the 881 included studies, we extracted and described the number, source, standardization, framing, and availability of fake news stimuli. Our findings suggest a large degree of stimuli variability, with high rates of example and full set availability. Most frequently, research stimuli were about politics and were framed as a social media post, and examples were given in-text. We also evaluated ratios of real to fake news stimuli, where an equal number of real and fake articles was the most frequent ratio. We recommend continued transparency, offering suggestions for open science practices following copyright law and considerations of generalizability regarding fake news in research.
Language-based affect assessments capture experiment-induced changes beyond rating scales
Oskar BĂĄng, Zhuojun Gu, August H. Nilsson, Veerle C. Eijsbroek, Clara Wiebel, Adithya V. Ganesan, Katarina Kjell, H. Andrew Schwartz, Oscar N. E. Kjell
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Language-based assessments (LBAs) have shown strong convergent and criterion validity cross-sectionally and longitudinally. However, no study has tested whether LBAs of affect can capture experiment-induced changes (i.e., causal validity ). In this randomized mixed-design experiment, participants ( N = 892) completed a mood induction procedure comprising different conditions ( church, shopping mall, and park ) using physical locations ( N = 153) and online video clips ( N = 739). Participants reported their pre- and post-induction affect through open-ended responses and ratings (i.e., the Positive and Negative Affect Schedule; PANAS). Using large language models and machine learning, we compared how accurately PANAS and language responses classified the experimental conditions. The LBA derived from the language responses exhibited significantly higher accuracy compared to the PANAS scores in classifying the conditions in both the training set (cross-validated AUC = .74 vs. .63), the online holdout set (AUC = .76 vs. .70), and the offline holdout set (AUC = .67 vs. .53). Apart from showing higher predictive accuracy, we assessed LBAs of specific affective dimensions (valence, arousal, and basic emotions). These analyses revealed that positive valence was most strongly expressed in the park condition, while anger was particularly elevated in the mall condition. We also visualized language differences across conditions, showing more calm and peaceful expressions in the park condition and more tired and bored expressions in the mall condition. These findings support the causal validity of language-based affect assessments and their ability to provide insights into affective states. We discuss the potential of using LBA in experimental psychological research.
Calibrating fit-index cutoffs for sparse ordinal CFA: Scenario-specific thresholds and operating characteristics
Liu Liu, Jin Liu, David Okech
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Sparse ordinal response patterns can shift the sampling distributions of common confirmatory factor analysis (CFA) fit indices, making fixed heuristic cutoffs difficult to interpret across design conditions. We conducted a Monte Carlo simulation to calibrate scenario-specific cutoffs for the comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) in sparse five-category ordinal CFA and to evaluate their operating characteristics. Data were generated from a three-factor population model with one cross-loading. For each dataset, we fitted a Correct model, a Local Misfit model omitting the cross-loading, and a Severe Misfit model combining two factors. We varied estimator (robust maximum likelihood [MLR], mean- and variance-adjusted maximum likelihood [MLMV], weighted least squares mean- and variance-adjusted maximum likelihood [WLSMV]), sample size ( N = 250, 500, 1,000), loading strength ( $$\lambda$$ λ = .50 vs. .80), sparsity pattern, sparse-item scope, and category handling (original vs. collapsed). Cutoffs were calibrated from Correct-model distributions at $$\alpha$$ α = .05, using lower-tail quantiles for CFI and TLI and upper-tail quantiles for RMSEA and SRMR. Calibrated thresholds were strongly scenario-dependent. RMSEA and SRMR cutoffs were generally far below conventional benchmarks, whereas CFI and TLI cutoffs varied with sample size and loading strength, falling below .95 in lower-information settings and approaching boundary values in larger-sample and high-loading settings. Detection was substantially lower for Local Misfit than for Severe Misfit. A calibrated CFI-RMSEA AND rule provided a transparent multi-index operating profile, with false-rejection and detection trade-offs that varied across scenarios. Collapsing produced modest changes for robust ML estimators but increased WLSMV detection in lower-information settings. We provide cutoff tables, operating-characteristic summaries, an interpolation aid, and an applied illustration to support scenario-calibrated fit evaluation in sparse ordinal CFA.
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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Listen to your better angles: Horizontality, verticality, and cardinality in visual scanning and foraging
Marcos Bella-Fernández, Alicia Ferrer-Mendieta, Beatriz Gil-Gómez de Liaño
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The Japanese Sentence Corpus: Eye-movement data on sentence reading with word predictability norms
Yuqi Hao, Hiromu Sakai, Qingtong Li, Ming Yan
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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 .
Angular speed should not be used to test the speed–curvature power law
Adam Matic
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The developmental English Lexicon Project (d-ELP): Continuous word reading difficulty estimates of the 9,961 most frequently printed English words for US children
Donald L. Compton, Jay G. Rueckl, Laura M. Steacy, Noam Siegelman, Ashley A. Edwards, Yaacov Petscher, Nicole Patton-Terry, Matthew J. Cooper Borkenhagen, Chelsea Starke
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The developmental English Lexicon Project (d-ELP) is a database comprising word reading data for 9,961 of the most frequently printed English words for children, including continuous item response theory (IRT)-based estimates of word reading difficulty, accompanying word characteristics, and raw trial-level data. Data on isolated word reading accuracy were collected from 1,907 children in grades 1–5 across multiple US sites. Each child read a list of words ranging from 250 to 500 items depending on reading skill (less proficient children were often unable to finish the entire list of words), with each list representing the full distribution of word difficulty oversampled for words at the child’s reading level. Each child also attempted 99 linking items representing the full distribution of word difficulty. A total of 631,322 responses were collected, with the average child providing 336. For the final sample of words, the average number of responses per word for the 9,862 non-linking items was 47.14 (range of 10–592), and for the 99 linking items the average was 1,681.42 (range of 1,083–1,907). A Rasch model, with planned missing data design, was fit to children’s responses on the word list and linking item responses. This introduction describes the motivation for this project, the methods used for data collection, the analytic framework used to establish a continuous measure of word reading difficulty across words, descriptive statistics of the relational database and initial behavioral results, and a publicly available search engine providing searchable access to word difficulty ratings and associated word-level statistics.
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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Beyond Curiosity: Habit and Organizational Learning Capability as Predictors of Shadow AI
Nolan Lovett, Mohan Yang
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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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From being watched to working the numbers: Dataveillance and the consequences of data crafting
Ward van Zoonen, Jeffrey W. Treem
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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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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

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.

Psychological Methods

Integrating artificial intelligence into back-translation: Opportunities, constraints, and recommendations for psychological research.
Julian Decius, Emily Kleszewski, Hannah Louisa KrĂĽger
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Psychological Science

The Gambler’s Fallacy Fallacy Fallacy (Commentary on Xiang et al., 2025)
Daniel Banki, Uri Simonsohn
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Xiang et al. concluded that the gambler’s fallacy does not originate in biased beliefs. Reanalyzing their data, we arrived at the opposite conclusion. We show, more generally, that their main analysis—examining whether the median belief is biased—is not diagnostic of the presence of the gambler’s fallacy, whereas our test of the share of probability estimates for the streak ending above or below 50% is diagnostic. We also report results from a new study in which at least 80% of gambler’s-fallacy predictions originated in biased beliefs.

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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