I checked 15 psychology journals on Wednesday, June 03, 2026 using the Crossref API. For the period May 27 to June 02, I found 66 new paper(s) in 10 journal(s).

Behavior Research Methods

Enhancing propensity score analysis with data missing not at random: Introducing dual-forest proximity imputation
Yongseok Lee, Walter L. Leite
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Researchers using propensity score analysis (PSA) to estimate treatment effects using secondary data may have to handle data that are missing not at random (MNAR). Existing methods for PSA with MNAR data use logistic regression to model the missing data mechanisms, thus requiring manual specification of functional forms, and are difficult to implement with a large number of covariates. To overcome these limitations, this study proposes alternatives to existing methods by replacing logistic regression with a random forest. Also, it introduces the dual-forest proximity imputation method, which leverages two types of proximity matrices of random forest techniques and incorporates missingness pattern information in each matrix. Results from a Monte Carlo simulation show dual-forest proximity imputation’s enhanced bias reduction with various types of MNAR mechanisms as compared to existing and alternative methods. A case study is also provided using data from the National Longitudinal Survey of Youth (Enders, 1979) (NLSY79).
Synthesis of single-case design mediation effects using two-stage multilevel modeling
Mariola Moeyaert, Milica Miočević, Yaosheng Lou, Matthew J. Valente
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The fundamentals of eye tracking, Part 7: Determining data quality
Diederick C. Niehorster, Marcus Nyström, Roy S. Hessels, Jeroen S. Benjamins, Richard Andersson, Ignace T. C. Hooge
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Understanding the quality of eye-tracking recordings, often characterized using accuracy, precision, and data loss, is crucial for the interpretation of eye tracking data. Eye-tracking data quality can furthermore place fundamental limits on what studies can be conducted with an eye tracker, and one may be required to report eye-tracking data quality when publishing a study. However, how does one determine the quality of eye-tracking data? This article provides an overview of operationalizations of accuracy, precision, and data loss and practical advice for determining eye-tracking data quality. Furthermore, the programming code for calculating various quality metrics for a segment of eye-tracking data is provided in MATLAB, Python, and R. Also provided is ETDQualitizer, a tool designed to enable anyone to easily determine the data quality of their recordings. We provide a version that is browser-based ( https://dcnieho.github.io/ETDQualitizer ) and enables determining eye-tracking data quality without installation or programming, while ensuring data privacy by running entirely locally. ETDQualitizer is further provided as a MATLAB, Python, and R library ( https://github.com/dcnieho/ETDQualitizer ) that can be integrated in one’s analysis scripts. We hope that this article enables any researcher to determine, critically evaluate, and report on eye-tracking data quality, and that it spurs researchers to adopt a data quality perspective in all their future eye-tracking studies.
Adding volition to word processing: Expected utility norms for 80,000 English words and multiword expressions
Andrew Wang, Marc Brysbaert, Fritz GĂŒnther
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This study examined the concept of word usefulness by analyzing expected utility ratings for over 80,000 English words and multiword expressions. Participants used best–worst ratings to indicate how useful it is to know each word/expression. Our findings show a high level of agreement regarding the usefulness of words and expressions. Stimuli were rated as more useful if they were more frequent, widely known, learned early in life, and central to the semantic network. Concreteness had a substantial negative correlation, indicating that abstract words in general received higher utility scores than concrete words. Positive stimuli received slightly lower utility scores than negative stimuli. Expected utility was a good predictor of which words are known to speakers of English as a first and second language, but did not contribute to predicting response times to known words. These findings suggest that expected utility is a variable affecting which words are likely to be learned, but does not affect word processing times (much). The expected utility scores are freely available for research and education.
Implementing multiple implicit association tests in Qualtrics: A guide and demonstration using balanced identity design theory
Alyssa Ream, Anna Woodcock, Sarah Zlatkovic, Rachelle M. Pedersen, Ashley Bonilla, Hannah Middleton, Paul R. Hernandez, P. Wesley Schultz
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Interest in implicit processes, such as attitudes and identities, has grown in behavioral sciences since Greenwald and colleagues (1998) developed and introduced the Implicit Association Test (IAT). The IAT was developed to measure these processes, which has become more accessible with the popularity of Qualtrics as a data collection tool, especially through methods and code provided by Carpenter et al. (2019). They demonstrated how to construct a single IAT for use on the Qualtrics platform, providing research resources for creating IATs to examine concept-pair associations. However, there is no guidance on constructing multiple IATs on survey software platforms for research that requires two or more IATs in a single study. This paper extends Carpenter et al.’s (2019) work by outlining the process of developing, implementing, and utilizing multiple IATs within a single Qualtrics survey. We provide a tutorial using examples from our research using the Balanced Identity Design (BID) framework, including step-by-step written and visual instructions and templates, instructions for planning and building multiple IATs and dynamically presenting them in a single Qualtrics survey and code for processing IAT data. We also demonstrate the utility of the multiple IAT approach by reporting a short study utilizing BID theory wherein we measure implicit racial/ethnic identity, STEM identity, and race/ethnicity-STEM associations in a single study via three IATs.
Know when to trust: Making AI scoring more reliable for educational assessment
Peter Organisciak, Selcuk Acar
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Robust Bayesian hypothesis testing with the hierarchical EZ-DDM
Adriana F. Chåvez De la Peña, Eunice Shin, Joachim Vandekerckhove
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The EZ-diffusion model (EZ-DDM) uses a method of moments to provide closed-form estimators for the three-parameter drift-diffusion model from summary statistics. In previous work, we showed that using the sampling distributions of these statistics enables the implementation of hierarchical EZ-DDM extensions, supporting scalable Bayesian inference in cognitive psychometrics applications. However, the summary statistics used in EZ-DDM implementations (the mean and variance of the response time distribution) are sensitive to contaminant data points, limiting its utility in real-world applications. To address this, we propose a variation on the EZ-DDM implementation in which the summary statistics are replaced with robust alternatives, substituting mean RT with median RT and RT variance with an estimate derived from the interquartile range. We explore and evaluate the effectiveness of this substitution through simulation studies using a within-subject t test design across varying sample sizes and effect sizes. We show that the robust variant matched the diagnostic accuracy of the EZ-DDM implementation on uncontaminated data while maintaining diagnostic accuracy under contamination, unlike the standard model. This extension preserves efficiency while adding robustness in real-world applications. We recommend using the robust EZ-DDM in practical applications.
Making the impossible possible: Leveraging built-in features for non-intrusive and accurate Apple Screen Time tracking through ASTER
Marijn Martens, Kyle Van Gaeveren
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Computers in Human Behavior

Generic title: Not a research article
Corrigendum to ‘Interplay of agenda setters in the digital age: The associative issue network between news organizations and political YouTube channels’ [Computers in Human Behavior 155 (2024), 108169]
Bumsoo Kim, Lin Han, Yonghwan Kim
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Understanding the Efficacy of Online Bystander Intervention when Journalists are the Targets
Kathleen Searles, Rebekah Tromble
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The role of arousal and valence in predictions of short video virality: A psychophysiological perspective
Danila Shelepenkov, Vladimir Kosonogov
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Longitudinal changes in gamers’ profiles and gaming disorder risk
CĂĄtia Martins Castro, Sophia Achab, David Dias Neto
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The Ties that (Healthily) Bind: Playing in Groups Increases Game Loyalty but Decrease Addiction
Shih-I Tai, Thi Tuan Linh Pham, Tzu-Ling Huang, Alexander S. Dennis, Chun-Hao Liu, Gen-Yih Liao, Ching-I Teng
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When conversational AI personalises too much: Refining privacy calculus for bundled interactional cues in AI-mediated disclosure
Khanh Duy Phan, Bao Quoc Truong-Dinh
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From Social Trust to AI Technology Acceptance: A Panel Study of AI-Powered Autonomous Vehicle Adoption in Singapore
Hongjie Tang, Shirley S. Ho, Liang Chen
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Unraveling the behavioral and autonomic dynamics of defensive states in humans in an unconstrained, immersive virtual world
Alma-Sophia Merscher, Lea K. Hildebrandt, Jasin Sahraoui, Matthias Gamer
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Measuring machine companionship experiences: Scale development and validation for AI companions
Jaime Banks
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Attention hijacked: How social media notifications disrupt cognitive processing
Hippolyte Fournier, Arnaud Fournel, François Osiurak, Olivier Koenig, Flora Pùris, Vivien Gaujoux, Fabien Ringeval
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Esports and VR: how does it change EEG spectral dynamics of attention shift and maintenance?
Alena Ovakimian, Ekaterina Karimova
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Training and oversight of algorithms in social decision-making: Algorithms with prescribed selfish defaults breed selfish decisions
Terence D. Dores Cruz, Mateus A.M. de Lucena
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How digital literacy shapes the conversion of political awareness into participation: A cross-national comparative study of Mongolia and China
Gavaa Zanabazar, Qaiser Mohi Ud Din, Tao Hong
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From iron to diamond: Collaborative behavior development across competitive tiers in League of Legends
Jimoon Kang, Seongcheol Kim
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Framing responsibility: Human and AI agent effects on apology effectiveness in service failures
Jihyun Soh, Eunice Kim
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Virtual characters, real emotions: Avatar identification and psychological transformation across esports and K-pop fandoms
Yi-Ting Huang, Yu-Chiao Huang, En-Chia Lin
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User cognitive fit in human-AI interaction: Exploring the link between input representation and generative output complexity
Hechang Cai, Jinlai Zhou
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Offended by the algorithm: The hidden interpersonal costs of clients seeking AI second opinion
Gerri Spassova, Mauricio Palmeira
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AI-generated image-based sexual abuse: Perpetration and consumption across three regions
Rebecca Umbach, Nicola Henry, Renee Shelby, Gemma Stevens, Kwynn Gonzalez-Pons
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AI byline, human content: Exploring how source and message framing shape news perception
Jino Chung, Jihye Lee
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Stimulated or saturated? Biometric analysis of augmented sport experiences among young adults: The role of hedonic innovativeness and repeated exposure
Yongjae Kim, Jin Woo Ahn
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Integrating technology acceptance, self-determination, and self-regulation: A structural model of generative AI-supported learning and competence
Shu Ching Yang
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Official onsite event versus unofficial streaming: Understanding the wellbeing formation in esports spectatorship
Sungkyung Kim, Hee Jung Hong
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Evaluating human-based and large language model-based content analysis: A case study on negative word-of-mouth classification in social media
Yolande Yunhsiou Yang, Chih-Chien Wang, Pau-Lin Chou, Chieh-Yu Tien, Jia-Ci Wen, Nien-Hsin Chen
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Rethinking targeting strategies for SMEs: How artificial intelligence and audience breadth drive advertising performance
Minjeong Ham, Jaeyoung Park
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Journal of Experimental Social Psychology

Fairness matters: The impact of leaders' emotional expressions on subordinates' behavior
Adili Abudukadier, Zhikun Zhou, Zhijun Cheng, Xudong Wang, Yangmei Luo, Xuhai Chen
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Journal of Personality and Social Psychology

Face the difference: Metacontrast as an affordance to spontaneous social categorization.
Verena Heidrich, Felicitas Flade, Roland Imhoff
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The environment impressions model.
Travis Lim, Eric Hehman
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The uniquely powerful impact of explicit, blatant dehumanization on support for intergroup violence.
Alexander P. Landry, Isaias Ghezae, Ramzi Abou-Ismail, Sarah Spooner, River J. August, Charlotte Mair, Anya Ragnhildstveit, Wim Van den Noortgate, Michele J. Gelfand, Paul Seli
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Are the metatraits fact or artifact? Ruling out alternative explanations for the higher-order factors of the Big Five.
Colin G. DeYoung, Ming Him Tai, Edward Chou, Boris Mlačić
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Conversations about boring topics are more interesting than we think.
Elizabeth N. Trinh, Nicole Thio, Nadav Klein
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Transcending embarrassment: On the reputational benefits of laughing at yourself.
Selin Goksel, Ovul Sezer, Jonathan Z. Berman
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Security priming in everyday life: How do symbols of close others support attachment in adulthood?
Karl E. Conroy, R. Chris Fraley
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How group personality composition affects person and group outcomes: An integrative analysis using the group actor–partner interdependence model.
Eva Bleckmann, Richard Rau, Oliver LĂŒdtke, Sascha Krause, Jenny Wagner
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On the relationship between indirect measures of Black versus White racial attitudes and discriminatory outcomes: An adversarial collaboration using a sample of White Americans.
Jordan R. Axt, Paul Connor, Suzanne Hoogeveen, Cory J. Clark, Michelangelo Vianello, Joanna N. Lahey, Adam Hahn, Jeffrey To, Richard E. Petty, Thomas H. Costello, Gregory Mitchell, Philip E. Tetlock, Eric Luis Uhlmann
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Multivariate Behavioral Research

betaselectr : Selective (and Proper) Standardization in Structural Equation Models
Rong wei Sun, Florbela Chang, Wendie Yang, Shu Fai Cheung, Sing-Hang Cheung
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When Deep Learning Outperforms Conventional Models in Long-Term Forecasting
Young Won Cho, Sy-Miin Chow, Danielle Symons Downs, Steriani Elavsky
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Flexible Exploratory Item Factor Analysis Models with Estimation Using Variational Autoencoders
Youngjin Han, Ji Seung Yang, Yang Liu
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Personality and Social Psychology Bulletin

When You’re With Me, Baby, the Skies will be Blue for All My Life? A Dyadic Longitudinal Study of Relationship Happiness Through Midlife
Georg Henning, Rebekka Weidmann, Dikla Segel-Karpas, Sophie Potter, Jenna WĂŒnsche
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Romantic relationships are critically important for a range of psychosocial outcomes across the lifespan, but few studies have examined relationship happiness in midlife. We investigate the trajectories of relationship happiness of romantic couples in midlife and examine whether children in the household, work status, and gender predict these trajectories. Dyadic latent growth curve models were applied to six waves of longitudinal data from N = 2,363 mixed-gender romantic couples ( M age = 48.28, SD = 7.27) in the Swiss Household Panel. A random-intercept cross-lagged panel model was run to investigate interconnections within couples. Relationship happiness decreased slightly, with steeper declines among younger participants. Female gender, having children and continuously not working were associated with less happiness. At some waves, fluctuations in men’s happiness predicted fluctuations in women’s happiness and vice versa. We discuss the need for further research on interindividual differences as well as implications for improving relationship happiness during midlife.
Advice Across Cultures: How Autonomy, Dialecticism, and Responsibility Differentially Shape Advice Giving in the United States, China, and India
Zhenlan Wang, Namrata Goyal, Shagufa Kapadia, Joan G. Miller
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Advice-giving is a key form of social support, yet little is known about how its style varies across cultural contexts. Across three studies, we examined how Americans, Chinese, and Indians give, evaluate, and culturally transmit advice, drawing on the cultural logics of idiocentrism, dialecticism, and allocentrism. In Study 1, open-ended data revealed culturally distinct styles: autonomous advice among Americans, contingent advice among Chinese, and direct advice among Indians. In Study 2, participants preferred their culture’s normative style, with these preferences driven by culturally rooted motives: self-esteem for Americans (reflecting idiocentric values), option generation for Chinese (reflecting dialectical thinking), and fulfilling responsibility for Indians (reflecting allocentric values). Study 3 analyzed children’s storybooks, revealing that these advice styles are reflected in everyday cultural products. These findings suggest that advice-giving is a culturally embedded practice shaped by deeper moral and epistemic values, extending beyond the traditional individualism–collectivism framework.
A Multimethod Assessment of Spontaneous Behavioral Synchrony in Race- and Age-Concordant Versus Discordant Dyads
Morgan D. Stosic, Adele E. Weaver, Ken Fujiwara, Ishabel M. Vicaria, Derek M. Isaacowitz, Mollie A. Ruben
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This work examines the impact of race and age concordance on behavioral synchrony and self-reported rapport in social interactions, employing a multimethod approach across three studies ( N total = 450) and a mini meta-analysis. Behavioral synchrony was assessed with reliable human coders and OpenPose, and rapport was measured through self-reports. Results indicated that race- and age-concordant dyads exhibited significantly greater behavioral synchrony than discordant dyads, suggesting that shared visible social identities may be associated with smoother, more connected interactions. Synchrony effects were stronger when measured by human coders compared to OpenPose, potentially reflecting human coders’ biases related to perceived similarity between interactants or coders’ more nuanced ability to detect certain components of synchrony compared to technological approaches. No significant effects of race or age concordance on self-reported rapport were observed. These findings highlight an important factor in predicting the spontaneous emergence of behavioral synchrony and emphasize the value of integrating multimethod approaches.

Psychological Bulletin

Artificial intelligence as a partner in meta-analysis—Research agenda, user recommendations, and speed–accuracy tradeoffs: Commentary on Jansen et al. (2025).
Brooke N. Macnamara
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The association between reading anxiety and reading achievement: A meta-analysis and systematic review.
Rachelle M. Johnson, Maxine Schaefer, Cynthia U. Norris, Richard K. Wagner, Sara A. Hart
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Relationships between cognition and daily functioning in adults with bipolar disorder: A systematic review and multilevel meta-analysis.
Amanda McCleery, Gerhard Stefan Hellemann, Junghee Lee
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Perfectionism is accelerating over time: A cross-temporal meta-analytic review of 35 years of college student data.
Thomas Curran, Andrew Hill, Pia Marie Pose
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Psychological Methods

From the 1940s to 2020s: A review of the current state of forced-choice methodology.
Jake Plantz, Keith D. Wright, Jessica K. Flake
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Unlocking nonlinear dynamics and multistability from intensive longitudinal data: A novel method.
Jingmeng Cui, Fred Hasselman, Anna Lichtwarck-Aschoff
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Testing informative hypotheses in factor analysis models using bayes factors.
Xin Gu, Xun Zhu, Lijin Zhang, Junhao Pan
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A general Monte Carlo method for sample size analysis in the context of network models.
Mihai A. Constantin, Noémi K. Schuurman, Jeroen K. Vermunt
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Modeling intraindividual variability as a predictor with intensive longitudinal data.
Lijuan Wang, Xiao Liu
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Attributing individual-level causal effects using experimental and observational data: A primer.
Tim Kaiser, Stephen G. West, Steffi Pohl
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Variable selection for explaining interindividual heterogeneity in longitudinal growth trajectories.
Qian Zhang, Haochen Lei, Palmer Swanson, Hongyuan Cao, Elizabeth H. Slate
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Scoring assessments in multisite randomized control trials: Examining the sensitivity of treatment effect estimates to measurement choices.
Megan Kuhfeld, James Soland
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Testing and improving the robustness of amortized bayesian inference for cognitive models.
Yufei Wu, Stefan T. Radev, Francis Tuerlinckx
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A largely univariate framework for understanding multivariate analysis of variance.
R. Michael Furr
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A graph theory based similarity metric enables comparison of subpopulation psychometric networks.
Esther Ulitzsch, Saurabh Khanna, Mijke Rhemtulla, Benjamin W. Domingue
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Psychology of Music

Moving together, feeling together: Body coordination, pupil size, and musical togetherness in classical duo performance
Laura Bishop, Anna Niemand, Sara D’Amario, Werner Goebl
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This study investigated the relationships between bodily expression, bodily coordination, mental effort, and self-reported experiences of musical togetherness. Singer-pianist duos performed pieces of classical repertoire before and after rehearsing together. Video, head motion, respiration, and pupillometry data were collected. Afterwards, the performers watched a video of their post-rehearsal performance and made continuous ratings of how together they felt. They also provided written descriptions of the cues that informed their togetherness judgements. Our analyses showed stronger head coordination between partners and larger pupil diameter during high-togetherness phrases than during low-togetherness phrases for one of the pieces. Inhalation synchrony and quantity of head motion did not differ between high- and low-togetherness phrases. Pupil size was greater during pre-rehearsal performances than during post-rehearsal performances, suggesting that demands on attention might have reduced after players had jointly constructed a shared interpretation and grown more accustomed to performing together. A thematic analysis of written responses showed that performers’ concept of togetherness related to coherence in musical parameters, moving and breathing together, and sensations of feeling together, shared musical emotion, and effortlessness. We discuss these findings in relation to the musical togetherness model.
Temporal Dynamics of Intraparietal Sulcus Activation in Response to Tonal Uncertainty: An fMRI Study of the Diminished Seventh Chord
Chen-Gia Tsai, Ling-Yao Chien, Joshua Oon Soo Goh
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Tonality and harmony in music provide a structured framework to explore the neural mechanisms underlying uncertainty. The diminished seventh (dim7) chord, characterized by its multiple potential resolutions, serves as an ideal stimulus for examining the neural correlates of tonal uncertainty. This functional magnetic resonance imaging study investigated neural activity elicited by the dim7 chord in listeners with refined music listening skills, focusing on three harmonic progressions: CHANGE, modulating to a distantly related tonality; RETURN, resolving to the original tonality; and PERSIST, sustaining tonal ambiguity through repeated dim7 chords. A finite impulse response model was used to analyze responses in regions associated with uncertainty processing. The intraparietal sulcus (IPS) uniquely exhibited distinct temporal activation patterns across the three conditions. In CHANGE, IPS activity initially decreased, likely reflecting reduced confidence in the outdated tonal model’s predictions, followed by an increase as sensory evidence for a new tonality accumulated. In RETURN, IPS activity showed limited fluctuation. In PERSIST, a progressive increase in IPS activity may reflect heightened cognitive demands for maintaining and evaluating multiple tonalities. These findings deepen our understanding of predictive coding by highlighting the nuanced role of the IPS in processing uncertainty.

Psychology of Popular Media

The impact of fitspiration depicting different racial groups on Asian American women’s body dissatisfaction and appearance comparison.
Ruifei Zhang, Lauren A. Stutts
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