I checked 15 psychology journals on Sunday, October 18, 2026 using the Crossref API. For the period October 11 to October 17, I found 27 new paper(s) in 10 journal(s).

Advances in Methods and Practices in Psychological Science

Restructuring Scientific Papers for Human and AI Readers
Zhicheng Lin
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Scientific communication faces a dual crisis: Exponential publication growth—now accelerated by writing assisted by artificial intelligence (AI)—overwhelms human readers and reviewers, while fragmented evidence impedes automated synthesis. This fragmentation is especially acute in the behavioral and social sciences, where poorly standardized stimulus sets and jingle-jangle measurement fallacies (identical labels for distinct constructs; different labels for the same construct) hinder comparison, while underreported context conceals effect heterogeneity. Current AI tools can summarize papers but cannot reliably synthesize findings across incommensurable studies; they also risk perpetuating biases when trained on unstructured, unverified text. I propose restructuring scientific papers for two audiences: front-loaded narratives for time-pressed human readers, paired with research-object packages containing executable environments, semantic annotations, and tidy trial-level data. This design makes papers queryable: Readers can interrogate data and probe analytic choices, while the packages ground automated verification and AI-assisted manuscript evaluation—before submission or during peer review—in inspectable data and executable analyses rather than narrative claims alone. Such papers could become nodes in continuously updated evidence networks, automatically contributing effect sizes to living, versioned meta-analyses and allowing corrections and retractions to propagate through dependent syntheses. Widespread adoption will require institutions to credit research-object packages as scholarly contributions and invest in the open standards, infrastructure, training, and governance needed for their creation and long-term stewardship.
Registered Replication Report: Johns et al. (2005)
Andrea H. Stoevenbelt, Paulette C. Flore, Matus Adamkovic, Gabriel Banik, Rachel Bergers, Robert J. Calin-Jageman, Luis. A. Gomez, Joachim HĂŒffmeier, Megan N. Imundo, Scott D. Martin, Steven C. Pan, Lorraine A. T. Phillips, Jakob Pietschnig, Tilli Ripp, Jan Phillip RoĂ«r, Ivan Ropovik, Joyce Elena Schleu, Ann-Kathrin Torka, Bruno Verschuere, Martin Voracek, Luca Weiss, Jelte M. Wicherts
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“Stereotype threat” refers to the fear of being judged based on negative stereotypes about the performance of a certain group one identifies with. Numerous published studies have found that stereotype threat might lower mathematics-test performance among women. However, many studies have used suboptimal designs and analyses, the literature might be subject to publication bias, and the cross-cultural generalizability of the effect remains unknown. In this registered replication report, we describe the result of nine direct replications (total N = 1,709) of a representative stereotype-threat study by Johns et al., who found that threatened women (but not men) underperform when they are confronted with a mathematics test that is presented to measure gender differences and that this effect can be alleviated by altering test instructions. A multilevel analysis showed no significant interaction effect between stereotype-threat condition and gender and an observed gender gap only on the mathematics test, in which women performed worse than men (Hedges’s g = −0.26, SE = 0.11, 95% confidence interval [CI] = [−0.47, −0.05]). The stereotype-threat effect among women was virtually null and not significant ( N = 1,275, g = 0.04, SE = 0.08, 95% CI = [−0.11, 0.20]) and considerably smaller than the original study ( N = 45, d = −0.82, 95% CI = [−1.45, −0.18]). Secondary analyses yielded a main effect of gender composition and a random effect of lab on mathematics-test performance, but neither variable affected the interaction between stereotype threat and gender. The current results fail to replicate the stereotype-threat effect by Johns et al., hence casting doubt on the generalizability of the effect of stereotype threat on women’s mathematics performance.

Behavior Research Methods

Not that cloze: Sentence constraint is influenced by language and language dominance
Ana Bautista, Clara D. Martin
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In the fields of language processing and prediction, cloze tests are used to establish sentence constraint norms. The validity of cloze test results depends heavily on how closely the pre-test population matches the main study population, and yet there is limited evidence on how sociolinguistic variables may influence sentence constraint. Here, we investigate how language and language dominance influence sentence constraint at the lexical level by conducting a cloze test across different language groups and comparing response entropy. In Analysis 1, sentence constraint was similarly high for Spanish- and French-dominant speakers tested on the same translated sentences. However, their item-by-item alignment was moderate. In Analysis 2, non-dominant French speakers showed lower sentence constraint than dominant speakers overall. Dominant and non-dominant French speakers showed only moderate alignment, whereas the strongest alignment was observed between the two groups of non-dominant French speakers with different dominant languages. Our findings suggest that sentence expectations are shaped by language and language dominance. These findings are discussed considering theoretical proposals on the cloze test procedure, and offer informed practices for designing future language prediction studies across diverse speaker populations.
Identifying hidden cognate effects in L2 lexical retrieval tasks: A tutorial for stimulus design
Kelly Kendro, Alexander J. Holmberg
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Divine verification: Introducing a CAPTCHA-based religious priming technique
Fırat ƞeker, Ensar Acem, Rozelin Vurgun, Burak Dogruyol, Ozan Isler, Onurcan Yilmaz
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A topic-based framework for interpretable and sparse semantic representations
Ming Song, Qing Cai
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Engagement begins at recruitment: Comparing videoconference verification to passive online screening for participant retention and study engagement
Andrea L. Howard, Johanna Peetz
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Real-time assessment of temperature sensitivity, pain processing, and pain behavior across the lifespan: Integrating stimulus data, contact force, and subjective ratings
Heinz-JĂŒrgen Steinhoff, Besim Prenaj, Sebastian L. KĂŒbel, Annekatrin Steinhoff
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Assessments of temperature sensitivity and pain processing play an important role in psychological and psychiatry research, including with adolescent participants and across the lifespan. To ensure the validity and comparability of findings, experimental settings must be well tolerated and easily manageable for participants of all age groups and reliably monitored by the experimenter. These settings should promote reproducible procedures and support computer-assisted data acquisition and analysis to enable robust, efficient scientific evaluation. We present CAREST (Controlling and Recording Experiments in Sensitivity to Temperature), a PsychoPy-based tool for real-time assessment of temperature sensitivity, pain sensation, and pain response behavior via thermal induction. CAREST controls and communicates with a cold/hot plate and provides a touchscreen slider to assess pain sensation on a visual analog scale (VAS). The plate is placed on a platform with load cells to measure hand-plate contact force, providing an objective assessment of thermal contact between hand and plate surface during the experiment and participants’ approach versus avoidance tendencies in response to pain. CAREST continuously and synchronously records timestamped values of plate surface temperature, participants’ VAS ratings, and hand-plate contact force, enabling computation of several established and novel measures of pain sensitivity and related behavioral factors within a single experimental setting. In a pilot study with 94 participants, we examined heat and cold pain thresholds, intensities, tolerances, and hand-plate contact forces. The results highlight substantial heterogeneity in pain response behavior, with one out of five participants differing markedly from the modest average tendency toward pain avoidance.
Inclusive electroencephalography practices for Black and Muslim participation in a White non-Muslim-majority country: A co-produced tutorial
Damian Cruse, Ilham Razaie, Nyamesumpa Abbey, Natasha Bradbury, Yasmin Darawil, Hana Eldessoky, Fatima Farooq, Olivia Francis, Romy Froemer, Dorinda Lokko, Esther Nwakor, Amaliah-Saffron Odutola, Andrew Quinn, Nishita Sujan, Malaika Zabair
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Most electroencephalography (EEG) research is conducted in White-majority Western countries by White researchers. EEG practices therefore often explicitly and implicitly exclude minoritised groups, leading to low diversity in EEG data samples that deepens biases in interpretation and clinical utility. Because EEG electrodes must contact the scalp, Black people and Muslim women who wear a hijab (or, Hijabis) are two systematically excluded groups. As a group of EEG researchers, Black people, and Hijabis, here we describe co-produced practical approaches to overcoming EEG participation barriers, from recruitment to data collection, and provide co-produced text, photographs, and videos designed to break down these barriers. These approaches provide further tools for the EEG community to tackle systemic exclusion and foster better, more representative neuroscience.
The samplr package: A tool for modeling human cognition with sampling algorithms
Lucas Castillo, Yun-Xiao Li, Adam N. Sanborn
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Many models of cognition have sampling as a central component. Researchers have used them to explain human performance across a wide range of tasks involving perception, probability reasoning, and choice. One family of these models takes inspiration from sampling approaches developed in statistics, either by adjusting samples with prior beliefs or by implementing a sampling algorithm that overcomes the difficulty of independent samples at the expense of autocorrelation. While these models have been successful in modeling human behavior, their adoption has been slowed by the technical difficulties in applying them. Here, we introduce the  package, which includes a variety of these algorithms and cognitive models built upon them. We show how models in this package can match qualitative patterns found in human data, and walk readers through examples of how to compare these models quantitatively to human data. This tool aims to make these methods accessible to many more researchers.
An introduction to the methodological corpus of systematic observation and its quality assessment
Daniel LĂłpez-Arenas, Susana Sanduvete-Chaves, Salvador ChacĂłn-Moscoso
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Observational methodology (OM) has evolved into a rigorous and flexible mixed-methods framework for studying spontaneous behavior in natural settings. By systematically recording behavioral streams and converting qualitative data into quantitative parameters, OM provides ecologically valid evidence often unattainable in experimental designs. This article presents a comprehensive methodological framework that synthesizes the conceptual, methodological, and analytical foundations of OM, along with recent advances in quality assessment. It traces OM’s evolution from qualitative traditions to a mature scientific methodology grounded in technological innovations and robust analytical procedures. A central element is the taxonomy of observational designs, which guides decisions on instrument development, sampling, and data analysis. This paper examines the structure of observational instruments, highlighting the complementary roles of structured category systems and flexible field formats, as well as the growing use of hybrid instruments that combine theoretical rigor with practical flexibility. Key components of the QUAL-QUANT-QUAL sequence are described, including behavioral segmentation, qualitative data typologies, and their transformation into quantitative parameters such as frequency, order, and duration. Analytical techniques used in OM include classical statistics and three field-specific procedures: lag sequential analysis, polar coordinate analysis, and T-pattern detection, which together enable precise identification of recurrent behavioral patterns. The article also addresses quality assessment, reviewing key frameworks that promote transparency, standardization, and evidence synthesis across behavioral science domains. Overall, this work offers an updated reference for designing, implementing, evaluating, and synthesizing studies based on OM.
Accounting for response styles in rating data using IRT and IRTree models: A tutorial in R with applications to self-reported metacognitive competence
Thorsten Meiser, Esther Ulitzsch, Mirka Henninger
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Response styles refer to individual differences in the use of the item response format irrespective of item content. They include tendencies to favor or avoid midpoint or extreme response categories, which can distort the measurement of latent constructs. This tutorial provides an overview of IRT and IRTree models that accommodate response style factors, allowing researchers to analyze response-style effects and adjust latent-trait estimates for potential bias. In particular, we give a practical guide to using IRT and IRTree models for response styles and illustrate the workflow of model implementation, estimation, and interpretation with an empirical data set on self-reported metacognitive competence (Double, 2025). Throughout the tutorial, IRT and IRTree models are specified with standard tools, and parameter estimation is accomplished in the common framework of multidimensional IRT with the package (Chalmers, 2012; Chalmers et al., 2025). The empirical results demonstrate that IRT and IRTree models can disentangle substantive traits and response styles, and that model-based estimates of substantive traits are adjusted accordingly. More specifically, both model classes reveal pronounced correlations among latent trait dimensions and among response style factors, but only small correlations between traits and response styles. In addition, the model-based trait estimates show the expected patterns of amplification and shrinkage after adjusting for midscale and extreme response style. The tutorial concludes with a discussion of recent developments in psychometric modeling.
Decisions under uncertainty: A statistical framework for evaluating practical relevance in interval-based hypothesis testing
Paul Riesthuis, Robert A. Cribbie, Victoria Celio, Nataly Beribisky
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Computers in Human Behavior

Tracking Fitness, Comparing Bodies: Social Comparison and Exercise Activity Among College Students
Christina N. Selby, Kyle J. Holody, Elizabeth L. Carter
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Does AI encourage lax thinking, or does the human preference for validation make it that way?
Braden T. Hall, William Hart
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Understanding human detection of algorithmic bias through drift-diffusion modeling
Hannah Overbye-Thompson, Kristy A. Hamilton, Jacob Fisher
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Self-disclosure to intelligent virtual agents: A scoping review and the disclosure affordance–actualization–governance framework
Yimin Wang, Zhanghui Wang, Yu Zou, Yanping Song, Guobin Xia, Shaokang Chen, Haowei Guo, Chenxi Cao, Xinsheng Yang, Ziyan Deng, Jiaqi Li, Daojun Gong, Wenxiao Wang, Xin Lyu, Zhulin Tao, Xinyuan Huang
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Why this AI app? The impact of generative artificial intelligence on educators’ choices in educational apps
Emma Liptrot, Adam Kenneth Dubé
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Journal of Experimental Social Psychology

A preference for sameness and associated positive emotions in interindividual discrimination, even when based on chance
Xizhuo Zou, Fiona Kumfor, Evan Livesey, Leonie Schuppers, Eliane Deschrijver
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Multivariate Behavioral Research

Testing Group Differences in Nonlinear Growth Curve Modeling with Penalized Spline
Han Du, Yang Liu, Zhiyong Zhang
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Homogeneity-Pursuit Calibration of Item Response Theory Models with Limited Samples and Sparse Response Data
Shiyu Wang, Cong Cheng, Yuan Ke, Zichu Liu
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Organizational Research Methods

Triangulation as Methodology and Mindset: Developing Practice-Based Guidance for Organizational Research
Sarah Wittman, SĂ©bastien Point, Shiyu Yang
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Aiming to inform organizational research practice, we scrutinize how scholars in management and business studies report triangulation: the systematic integration of multiple data sources, methodologies, measures, investigators, and/or theories, usually with the objective of enhancing the robustness, validity, richness, and/or complexity of research findings. We analyze 1,209 articles from the UTD24 journal list, using thematic analysis to assess scholars’ use and justification of triangulation as a term of art that spans disciplinary boundaries and qualitative, quantitative, mixed-methods, and nonempirical methodological approaches. Our findings elucidate triangulation types (method, data, investigator, theory, and measures triangulation); convergence (validity and corroboration) and divergence (richness and complexity) perspectives; and reporting practices at the manuscript level. From this analysis and our examination of exemplars, we abstract triangulation's dual nature: as a root methodology—a foundational tool that transcends methodological boundaries and is deployable across the research process—and a research mindset—an epistemological orientation that engages reflexivity and multiplicity to achieve rigor. Based on our findings, we provide recommendations for how to plan for, conduct, report, and evaluate triangulation and discuss our contributions to organizational research methods.

Personality and Social Psychology Bulletin

Impressions of Deviant Disgust Expressions Are Functionally Specific
Kaelyn Sabree, Joshua M. Ackerman
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How do people perceive those who violate emotional expression norms? Across four studies, impressions of people displaying disgust “inappropriately” depended on how the deviation occurred. Targets who failed to show disgust toward disgust-eliciting stimuli (“flat deviants”) were judged as less healthy and lower in social status. Conversely, targets who expressed disgust to neutral stimuli (“react deviants”) were judged as less open-minded and less cooperative. These impressions tracked inferred behavioral affordances: flat deviants were seen as more likely to engage in pathogen-risky behaviors, whereas react deviants were not. Consistent with these judgments, affective deviants were differentially “punished”: flat deviants were rated as less fit and less likely to be recommended for jobs emphasizing health and hygiene, whereas react deviants faced these penalties for jobs emphasizing cooperativeness. Thus, perceivers do not simply condemn emotional norm violations; instead, they draw on expression–environment mismatches to make functionally specific trait judgments and potentially discriminatory decisions.

Psychological Methods

Structural-before/after-measurement approaches for estimating treatment effects in latent outcomes with causal machine learning.
Amota Ataneka, Benjamin Kelcey, Jean Baptiste Habarurema
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Psychological Science

Does Global Slowing Explain Age Effects in Inhibitory Control?
Jacob Sellers, Han Zhang, John Jonides, Taraz G. Lee
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A long-standing debate in the study of cognitive aging centers around the issue of whether aging results in a selective deficit in inhibitory control beyond general slowing of cognitive processing. Previous studies have obtained mixed findings, in part because the typical free-response-time paradigm lacks the specificity required to disentangle component processes underlying inhibitory control. Here, we apply a forced-response paradigm and computational model to test whether aging results in overall speed differences or a selective deficit in inhibitory control. Across two experiments, over 200 older adults and younger adults completed a forced-response arrow-flanker task. We found strong evidence that older adults demonstrate a global slowing of processing without any selective deficit in inhibitory control in this task. Furthermore, we demonstrate that global slowing is sufficient to explain the mixed findings in previous free-response-time studies. Overall, these results argue against a selective inhibitory-control deficit in older adults.
Artists Can Design Memory, and AI Can Predict It
Trent M. Davis, Yifei E. Chen, Wilma A. Bainbridge
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Can artists deliberately design artworks to be remembered—or forgotten? Across three experiments, we tested whether visual-memory outcomes can be shaped through artistic design and how they are predicted by artists, laypeople, and artificial intelligence. We launched a nationwide art contest in which artists submitted works intended to be memorable or forgettable. Using recognition tasks with adults based in the United States (119 from Amazon Mechanical Turk, 892 from Prolific, and 62 visitors from a Chicago gallery), we found that artists achieved their intended memory outcomes. However, their success was asymmetric: Memorability was more readily increased than reduced. A neural network, ResMem, consistently outperformed human observers and artists in predicting which artworks would be remembered. Data-driven analyses identified psychologically interpretable features that explained nearly half the variance in memory and aligned with ResMem’s predictions, but not with human judgments. Together, findings suggest that visual memorability can be engineered by humans, though asymmetrically, and is better captured by computational models.

Technology, Mind, and Behavior

Beyond accuracy: How humans evaluate legally correct but socially controversial legal advice from machines.
Benjamin Minhao Chen, Zhiyu Li
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