I checked 15 psychology journals on Tuesday, June 23, 2026 using the Crossref API. For the period June 16 to June 22, I found 28 new paper(s) in 11 journal(s).

Advances in Methods and Practices in Psychological Science

Rethinking Type S and Type M Errors
Daniël Lakens, Cristian Mesquida, Gabriela Xavier-Quintais, Sajedeh Rasti, Enrico Toffalini, Gianmarco AltoÚ
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Gelman and Carlin introduced Type S (sign) and Type M (magnitude) errors to highlight the possibility that statistically significant results in published articles are misleading. Although these concepts have been proposed to be useful both when designing a study (prospective) and when evaluating results (retroactive), we argue that these statistics do not facilitate the proper design of studies or the meaningful interpretation of results. Type S errors are a response to the criticism of testing against a point null of exactly zero in contexts in which true zero effects are implausible. Testing against a minimum effect while controlling the Type 1 error rate provides a more coherent and practically useful alternative. Type M errors warn against effect-size inflation after selectively reporting significant results, but we argue that statistical indices such as the critical effect size or bias-adjusted effect size are preferable approaches. We do believe that Type S and Type M errors can be valuable in statistics education, in which the principles of error control are explained, and in the discussion section of studies that fail to follow good research practices. Overall, we argue that their use cases are more limited than is currently recognized and that alternative solutions deserve greater attention.

Behavior Research Methods

How continuous is continuous enough? Comparing the reliability of continuous and discrete scales
Wei-Hung Yang, Yao-Ting Sung, Yeh-Tai Chou
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Designing a reliable rating scale with an appropriate number of options remains a central issue in social science measurement. Prior studies have reported inconsistent findings, with some suggesting that reliability is maximized at moderate response granularity and others indicating that finer granularity yields higher precision. These inconsistencies may partly reflect methodological differences, particularly the use of classical test theory or the discretization of continuous responses, both of which may underestimate the reliability of continuous scales. To address these issues, the present study reconceptualized discrete and continuous scales as points along a unified granularity continuum and examined reliability using the continuous rating scale model (CoRSM). Utilizing the CoRSA analytical framework (Chou et al., 2025), we conducted two complementary studies. Study 1 employed Monte Carlo simulations varying sample sizes ( N = 200 to 1,000), test lengths (11 to 61 items), and response formats ranging from three-point to continuous. Study 2 provided empirical validation with 3,434 junior high school students completing a career interest assessment across 10 response formats, ranging from five-point scales to continuous visual analogue scales (VAS). Across both studies, reliability generally increased as response granularity increased, although the empirical pattern was not strictly monotonic across all intermediate formats. In Study 2, segmented regression indicated that the breakpoint in diminishing returns occurred at approximately seven to eight response options within the discrete range examined. Meanwhile, the highest reliability estimates were observed for the 101-point and VAS formats. These findings clarify one methodological source of inconsistency in prior research.
A comparison of multivariate and univariate meta-analysis
Han Du
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Analyzing multidimensional formal dynamic models in psychology: A tutorial using graphical tools
Jingmeng Cui, Dieta Wagenmakers, G. Sander van Doorn, Fred Hasselman, Anna Lichtwarck-Aschoff
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Formal theories translate verbal theories into a mathematical representation, such as a coupled differential equation or other dynamical systems, intending to strengthen the deductive power of (clinical) theories and to formulate testable and novel hypotheses. Work in clinical formal theories mainly relies on simulations, which is an intuitive method for evaluating overall model performance, but may fall short of establishing a precise link between the mathematical properties of the model and the dynamic properties of its outcome. Moreover, when the model’s outcome contradicts clinical observations, it is unclear where the discrepancy lies and how to improve the model. In this article, we introduce formal mathematical techniques for graphical model analysis, including phase plane analysis, which allows identifying a system’s stable and unstable equilibria, and bifurcation analysis, a framework to delineate parameter regimes corresponding to qualitatively different dynamical outcomes for a model. Using two formal dynamic models in psychology (one for panic disorder and one for suicidal ideation), we illustrate those methods through an easy-to-use R package, deBif , with a graphical user interface. These examples demonstrate the importance of using graphical tools to investigate the hypothesized mechanisms of psychological systems.
Estimating multivariate longitudinal trajectories using mixed-effects models with crossed random effects
José Ángel Martínez-Huertas, Emilio Ferrer
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In this study we examine how a mixed-effects model with crossed random effects for individuals and variables estimates within- and between-variability in longitudinal multivariate trajectories from cohort-sequential designs. These designs are characterized by large proportions of planned missing data, and they usually require continuous-time metrics. Via simulations, we evaluated different model outcomes under various conditions regarding the size of clusters (individuals and variables) and the complexity of the trajectories. Results show that (a) this model can estimate the general trajectories (common to all individuals and variables) and their variability, plus the variable-specific trajectories through the predictions of the levels of the random factors; (b) the standard errors of the random effects are wide, yet they are important for making substantive decisions for specific variables; and (c) the model predictions can adequately forecast individual and variable-specific complete trajectories from just a few observations per individual. These results are supported in an empirical illustration using cognitive developmental data. These findings show that researchers can obtain complete individual trajectories for multiple variables throughout a target age range. The relative simplicity of this model in comparison with other alternatives makes it a promising and accessible tool for multivariate longitudinal data analysis.
A tutorial on causal network simulation and exploration using the causalnet R package
Kyuri Park, VĂ­tor V. Vasconcelos, Mike Lees
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Understanding how network structure influences system dynamics is essential for advancing psychological modeling. This tutorial introduces the causalnet R package, which enables researchers to systematically enumerate candidate directed networks by orienting a user-specified undirected or partially directed adjacency template. Users can impose directional constraints—such as those derived from prior theory or time-series models (e.g., graphical vector autoregressive models)—to restrict the space of admissible directed network configurations. The package supports dynamic simulations on these networks using either a theoretically grounded nonlinear model (Park et al., 2025) or a simplified linear alternative. Researchers can simulate system behavior and compare dynamic outcomes across structural configurations, parameter sets, or modeling assumptions. The primary audience is applied psychological and behavioral scientists who wish to evaluate competing theoretical accounts of symptom and behavior dynamics when causal direction is uncertain. Importantly, causalnet is not intended to identify a unique causal network from cross-sectional data; instead, it supports theory- and evidence-constrained enumeration of candidate directed structures and simulation-based screening of their dynamic implications against empirical targets. We illustrate how this workflow can be used to adjudicate competing psychological theories by linking structural assumptions to predicted dynamic signatures such as persistence and recovery. This approach facilitates a systematic exploration of how causal architecture and interaction dynamics give rise to the emerging dynamics of psychological processes over time.
Is “sky” bluer than “grass” is green? Word–color associations dataset for cognitive science
Eldad Keha, Avishai Henik, Eyal Kalanthroff
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Color plays a fundamental role in cognition, influencing perception, memory, and language processing. Although many studies have used word–color associations to examine cognitive processes, the selection of color-associated stimuli has often been arbitrary and rarely validated through independent behavioral measures. The current study developed and validated a dataset of color-associated object words for cognitive research. In Experiment 1, 298 participants provided subjective assessments for 143 words selected from 124 previous studies. For each word, participants indicated the most strongly associated color, rated the strength of the association, and selected the RGB value. The results revealed substantial variability across words in agreement and association strength. Based on these ratings, we constructed a dataset of 79 strongly color-associated words across 11 color categories. In Experiment 2, we validated the dataset in a UK sample ( N = 568) using a manual semantic Stroop task, in which words were presented in congruent or incongruent colors. Dataset words produced a reliable semantic Stroop effect, with faster responses to congruent than incongruent color-associated word pairings, and this effect was larger for dataset than non-dataset words. In Experiment 3, we replicated this validation pattern in an independent US sample ( N = 337), providing further evidence for the robustness and usefulness of the dataset for English-language cognitive research. Across the validation experiments, subjective association strength was related to the objective semantic Stroop effect. Together, the findings demonstrate that the current dataset provides an empirically validated set of color-associated words that can support research on perception, memory, semantic processing, and cognitive control.
Mind Melodies: An NLP platform to examine music cognition
Adolfo M. García, Alejandro Sosa Welford, Joaquín Ponferrada, Franco J. Ferrante, Camilo Avendaño, Gonzalo Pérez, Agustina Birba, Ivan Caro, Juan Francisco Saavedra, Daniel Escobar Grisales, Bruno Mesz
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Reliability, bias, and computational cost of estimating the Bayes factor using bridge sampling and the Savage–Dickey density ratio
Klaus Oberauer, Philipp Musfeld, Frederik Aust
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Bayes factors often require numerical estimation because closed-form solutions are unavailable. In six simulation studies, we explored the reliability, bias, and computational cost of two easy-to-use and broadly applicable methods: bridge sampling and the Savage–Dickey density ratios, based on Gaussian, logspline, and spline-smoothed kernel density approximations of the posterior distribution, as well as conditional marginal density estimation. In generalized linear mixed effect models for normally and binomially distributed data, we explore the effects of the (1) number of MCMC samples from the posterior, (2) size of effects or magnitude of the Bayes factor, (3) number of participants, and (4) number of model parameters. Our findings suggest that, with enough MCMC samples, both methods yield reliable and accurate estimates across a wide range of conditions. However, with many model parameters, bridge sampling becomes computationally expensive and can be unreliable. In contrast, the Savage–Dickey density ratio scales well, remaining computationally efficient and reliable, even with many model parameters. However, Savage–Dickey density ratio requires careful consideration of posterior density estimation to mitigate bias while limiting the variability of Bayes factor estimates. We provide practical recommendations to guide researchers in selecting the most suitable estimation method for their applications.
From review to synthesis: A step-by-step methodological guide to systematic reviews and multilevel meta-analyses
Meilan Hu, Paye Shin Koh, Xun Ci Soh, Andree Hartanto, Nadyanna M. Majeed
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We provide a step-by-step guide on conducting a quantitative systematic review (i.e., meta-analysis) using the open-source programming language R , as well as conducting a multilevel meta-analysis, in contexts where effect sizes are non-independent (e.g., multiple studies from the same lab). Quantitative systematic reviews offer researchers a method for synthesizing large bodies of literature, helping clarify inconsistent findings, identify research gaps, and refine theoretical models. However, existing tutorials often assume prior knowledge and/or experience, often overlooking foundational concepts. To address this gap, a comprehensive walkthrough of the systematic review process is presented, covering pre-registration, literature search and retrieval, screening, risk of bias assessment, and data extraction following the PRISMA framework. We then present detailed guidance on how to conduct both traditional and multilevel meta-analyses in R . Specifically, the tutorial explains how to estimate overall meta-analytic effect sizes when effect sizes are independent (traditional meta-analysis) and when effect sizes are nested within labs (multilevel meta-analysis). Procedures for assessing heterogeneity, testing for publication bias, and conducting moderation analyses are also covered. To accompany this tutorial, we supplement annotated R scripts and R notebooks to support transparency, reproducibility, and accessibility for researchers of all levels of experience.
Synchronizing brains and hearts: A practical guide for caregiver–child fNIRS-ECG multimodal hyperscanning
Yelim Hong, Nicole J. Moore, Laura E. Quiñones-Camacho
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The study of synchrony—concordant neural, physiological, and behavioral activity between individuals—has advanced our understanding of co-regulation and socio-emotional development, particularly within caregiver–child relationships. While previous research has often examined neural and physiological synchrony separately, recent multimodal approaches highlight the importance of integrating these systems. This paper presents a comprehensive, practical guide to multimodal hyperscanning using functional near-infrared spectroscopy (fNIRS) and electrocardiography (ECG) in caregiver–child dyads. We outline theoretical foundations, methodological considerations, and detailed protocols for synchronizing fNIRS (via NIRx), ECG (via Mindware), behavioral video (via Mangold VideoSyncPro), and task stimuli (via PsychoPy) in a fully time-aligned manner. We describe equipment configurations, software integration, trigger-based synchronization workflows, and strategies to overcome common challenges in developmental psychophysiological research, particularly with young children. Drawing from our experience with children aged 3–7 years, we provide empirical benchmarks for feasibility, compliance, and data quality. This guide provides a practical, adaptable framework designed to lower the barrier to entry for multimodal hyperscanning and encourage its widespread use in developmental research. Integrating neural and physiological synchrony allows researchers to capture the dynamic, multi-systemic nature of caregiver–child interaction and opens new avenues for investigating mechanisms of co-regulation, emotional development, and risk for psychopathology.

Computers in Human Behavior

Blessed or not? The dual-path influence mechanism of intelligent elderly care service robots' roles on Chinese family caregivers' well-being
Caihua Yu, Siwen Xu, Tonghui Lian
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Combining Intergroup Contact and Outgroup Embodiment in Virtual Reality: An Exploratory Test of Backfire and Secondary Transfer Effects of Outgroup Embodiment
Matilde Tassinari, Akimi Oyanagi, Tomohiro Amemiya
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Adolescent Bystanders in Technology-Facilitated Sexual Dating Abuse: A Latent Class Analysis
Maria Vale, Denise Hines, Marlene Matos
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Perceptions and adoption of AI in public relations: Innovation attributes, threats, and practical implications
Sung-Un Yang, Cen April Yue, Arunima Krishna, Donald K. Wright
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Too Tired to Be Judged by Humans: Social Jetlag Increases Preference for AI through Social Anxiety
Xiaoyu Zhou, Yuxuan Chu, Liwei Zhang, Jianping Liang
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How augmented reality usage traces shape consumer decision-making in online food shopping: The roles of virtual taste and contamination sensitivity
Zhiying Xu, Xingyuan Wang, Gaojie Zhang, Chao Qi
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Young People Evaluating Online Information Credibility. Special Issue Synthesis
Nicolae Nistor, Yonty Friesem, Cristina Nistor, RareƟ Beuran
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Group Processes & Intergroup Relations

Vulnerable Online: Identifying Risk Profiles for Recruitment into Online Extremist Groups
Joshua Cloudy
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This study draws from the literature in group processes and intergroup relations, metacognition, motivational processes, and criminology to identify risk factors for joining an extremist group, classifies individuals into risk profiles, and examines how the risk profiles moderate efforts at recruitment into such groups. The results of a latent profile analysis ( N = 721) demonstrated the existence of three risk profiles (i.e., low, moderate, and high), and an experiment demonstrated that those in the low- and moderate-risk profile were significantly less likely to identify with a violent online political group compared to non-violent online political groups whereas those in the high-risk group were equally likely to identify with a violent or non-violent political online group. By taking a broader perspective, this study provides a more comprehensive understanding of individual susceptibility to being drawn into a violent online extremist group and has important implications for those seeking to combat online radicalization.

Journal of Experimental Social Psychology

Cognitive conflict in willful ignorance: A mouse-tracking study
Fiona tho Pesch, Anna Baumert
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Journal of Personality and Social Psychology

The impact of “relational” Artificial Intelligence on human well-being: A self-determination theory analysis.
Michael A. Irias, Norman B. Schmidt, Thomas E. Joiner, James K. McNulty
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Multivariate Behavioral Research

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies
Minho Lee, Yon Soo Suh
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Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions
Madison Shea Smith, Susan C. South
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Personality and Social Psychology Bulletin

Prospects of Downward Mobility Cause Status Anxiety and Life Dissatisfaction
Davide Melita, Matthias S. Gobel, Juan Matamoros-Lima, Rosa RodrĂ­guez-BailĂłn, Guillermo B. Willis
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Despite extensive research on upward mobility, the psychological consequences of perceived downward mobility remain understudied. Across two cross-sectional and two experimental studies ( N = 2,819), conducted in high-income, post-industrial economies, we investigated the effects of perceived upward and downward mobility on status anxiety and well-being. Across designs, downward mobility beliefs consistently increased status anxiety, which in turn mediated harmful effects on life satisfaction and related well-being outcomes. Upward mobility beliefs reduced status anxiety and produced a positive indirect effect on life satisfaction only in an experimental study with U.S. participants, but it yielded inconsistent effects across the remaining three studies. Our findings suggest that both upward and downward beliefs influence well-being through status anxiety, but the effects of downward mobility beliefs are stronger and more consistent.

Psychological Methods

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.
Mischa von Krause, Stefan T. Radev
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Three-level vector autoregressive models.
Yue Xiao, Hongyun Liu, Zhiyong Zhang
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Psychological Science

Eye Glint as a Novel Perceptual Cue in Human Vision
Gwenisha J. Liaw, Colin J. Palmer
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A subtle yet ubiquitous feature of the human face is eye glint —specular reflections from the surface of the eye that vary with the position of light sources in the environment. This study tested whether eye glint influences face perception, particularly in how observers perceive the gaze direction of a person they are viewing. Adult participants viewed computer-rendered face images that varied in eye direction, head rotation, and illumination. The presence of eye glint had little influence on the accuracy or precision of perceived gaze direction when faces were viewed under simplified conditions. However, biases in perceived gaze direction caused by changes in head orientation or illumination direction were reduced when eye glint was present relative to when it was absent. This suggests that eye glint can help an observer to maintain constancy in gaze perception despite variability in the appearance of the eye region that occurs across viewing conditions.

Psychology of Popular Media

Content and context correlates of problematic media use in young children.
Shayl F. Griffith, Sarah E. Domoff
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