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              <p>Free dyadic dance is an ecological context in which people spon-taneously synchronize and imitate each other. Yet, automaticallymeasuring these mutual behavioral adjustments remains challeng-ing. Here, we compare a data-driven approach with human per-ception for detecting motion Interpersonal coordination (IC) indyadic dance. Using a motion capture dataset where visual and au-ditory coupling between dancers was experimentally manipulated,we formulated binary classification tasks contrasting coupled anduncoupled partners. We benchmarked machine learning modelsfor multivariate time series classification. Then we compared theirpredictions with perceptual judgments from an online experiment(N = 87). Machine learning models are evaluated on two types ofinput data: raw velocities and features based on the synchrony ofpairwise sensors across the two dancers. Results show that models’performance improved significantly when using synchrony featuresover raw velocities. Humans reached scores similar to models usingraw velocities(≃60% accuracy), with substantial inter-agreements(≃70%). Correlational analysis with variables derived from the mu-tual quantity of motions (QoM) of dancers revealed a human biastoward judging partners with similar QoM as coupled.</p>
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