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Record W4387182219 · doi:10.1016/j.isci.2023.108099

Embodied perspective-taking enhances interpersonal synchronization: A body-swap study

2023· article· en· W4387182219 on OpenAlexaff
Mattia Rosso, Bavo Van Kerrebroeck, Pieter‐Jan Maes, Marc Leman

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsMcGill University
FundersUniversiteit GentUniversité de Lille
KeywordsPerspective (graphical)Embodied cognitionInterpersonal communicationCognitive psychologyPsychologyPerspective-takingSynchronization (alternating current)Computer scienceCognitive scienceCommunicationSocial psychologyEmpathyArtificial intelligence

Abstract

fetched live from OpenAlex

Summary Humans exhibit a strong tendency to synchronize movements with each other, with visual perspective potentially influencing interpersonal synchronization. By manipulating the visual scenes of participants engaged in a joint finger-tapping task, we examined the effects of 1st person and 2nd person visual perspectives on their coordination dynamics. We hypothesized that perceiving the partner's movements from their 1st person perspective would enhance spontaneous interpersonal synchronization, potentially mediated by the embodiment of the partner's hand. We observed significant differences in attractor dynamics across visual perspectives. Specifically, participants in 1st person coupling were unable to maintain de-coupled trajectories as effectively as in 2nd person coupling. Our findings suggest that visual perspective influences coordination dynamics in dyadic interactions, engaging error-correction mechanisms in individual brains as they integrate the partner's hand into their body representation. Our results have the potential to inform the development of applications for motor training and rehabilitation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.058
GPT teacher head0.378
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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