Successful coordination emerges from aligned self-predictions in a dynamic motor interaction.
A central challenge in understanding joint action is explaining how individuals achieve successful coordination in dynamic, real-world settings. Although coordination is thought to depend on anticipating future events, including the actions of others, the precise contribution of such predictive proc...
| Publicado en: | Communications Psychology Vol. 4; no. 1; pp. 1 - 14 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
8/20/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196364799&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196364799 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27319121 N51Q jtl: Communications Psychology issn: 27319121 maglogo: N pubinfo: dt: 8/20/2026 vid: 4 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 196364799 196364799 196364799 10.1038/s44271-026-00523-7 196364799 ppf: 1 ppct: 13 formats: tig: atl: Successful coordination emerges from aligned self-predictions in a dynamic motor interaction. aug: au: Putra, Prasetia Utama Kano, Fumihiro affil: https://ror.org/0546hnb39 Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany sug: subj: Psychomotor Performance Eye Movements Interpersonal Relations Task Performance and Analysis Funding Source Germany Human Male Female Adult Comparative Studies Motion Capture Eye Movement Measurements Electrocardiography Motor Skills Social Behavior T-Tests Correlation Coefficient Poisson Distribution Confidence Intervals Descriptive Statistics Predictive Value of Tests Machine Learning Adult: 19-44 years Male Female ab: A central challenge in understanding joint action is explaining how individuals achieve successful coordination in dynamic, real-world settings. Although coordination is thought to depend on anticipating future events, including the actions of others, the precise contribution of such predictive processing remains unclear, since most existing evidence comes from simplified laboratory tasks that infer anticipation indirectly from reaction times. To address this gap, we studied 70 participants (forming 64 pairs) during solo and joint sessions of a turn-taking ball-hitting task while recording multimodal behavioral, physiological, demographic, and social measures. We found that successful coordination was most strongly associated with anticipation of one's own action outcomes, outperforming physiological synchrony, motor behavior, demographic characteristics, and social closeness. Partners whose predictions of their own action outcomes were more closely aligned coordinated better, likely because each behaved in ways the other implicitly expected. A Bayesian generative model further showed that well-coordinated pairs relied more heavily on prior expectations than on incoming sensory evidence when generating predictions during joint action. These results suggest that efficient coordination in dynamic real-world tasks emerges primarily when individuals' internal models for predicting their own actions are well aligned across partners. Our quantitative multimodal approach provides a framework for disentangling the contributions of predictive, physiological, motor, and social factors to coordination in naturalistic interactions. Combining eye-movement, body-kinematics, heart-rate, and individual-characteristic data with machine learning, this study shows that coordination during dynamic motor interaction reflects how similarly partners predict their own actions. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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