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Unobtrusive tracking of interpersonal orienting and distance predicts the subjective quality of social interactions
Author(s) -
Juha M. Lahnakoski,
Paul Forbes,
Cade McCall,
Leonhard Schilbach
Publication year - 2020
Publication title -
royal society open science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 51
ISSN - 2054-5703
DOI - 10.1098/rsos.191815
Subject(s) - interpersonal communication , proxemics , psychology , gaze , quality (philosophy) , cognitive psychology , conversation , interpersonal relationship , social relation , motion (physics) , social psychology , computer science , communication , artificial intelligence , philosophy , epistemology , psychoanalysis
Interpersonal coordination of behaviour is essential for smooth social interactions. Measures of interpersonal behaviour, however, often rely on subjective evaluations, invasive measurement techniques or gross measures of motion. Here, we constructed an unobtrusive motion tracking system that enables detailed analysis of behaviour at the individual and interpersonal levels, which we validated using wearable sensors. We evaluate dyadic measures of joint orienting and distancing, synchrony and gaze behaviours to summarize data collected during natural conversation and joint action tasks. Our results demonstrate that patterns of proxemic behaviours, rather than more widely used measures of interpersonal synchrony, best predicted the subjective quality of the interactions. Increased distance between participants predicted lower enjoyment, while increased joint orienting towards each other during cooperation correlated with increased effort reported by the participants. Importantly, the interpersonal distance was most informative of the quality of interaction when task demands and experimental control were minimal. These results suggest that interpersonal measures of behaviour gathered during minimally constrained social interactions are particularly sensitive for the subjective quality of social interactions and may be useful for interaction-based phenotyping for further studies.

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