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The infant motor system predicts actions based on visual statistical learning

Monroy, Claire, Meyer, Marlene, Schroer, Lisanne, Gerson, Sarah and Hunnius, Sabine 2019. The infant motor system predicts actions based on visual statistical learning. NeuroImage 185 , pp. 947-954. 10.1016/j.neuroimage.2017.12.016

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Abstract

Motor theories of action prediction propose that our motor system combines prior knowledge with incoming sensory input to predict other people's actions. This prior knowledge can be acquired through observational experience, with statistical learning being one candidate mechanism. But can knowledge learned through observation alone transfer into predictions generated in the motor system? To examine this question, we first trained infants at home with videos of an unfamiliar action sequence featuring statistical regularities. At test, motor activity was measured using EEG and compared during perceptually identical time windows within the sequence that preceded actions which were either predictable (deterministic) or not predictable (random). Findings revealed increased motor activity preceding the deterministic but not the random actions, providing the first evidence that the infant motor system can use knowledge from statistical learning to predict upcoming actions. As such, these results support theories in which the motor system underlies action prediction

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Publisher: Elsevier
ISSN: 1053-8119
Date of First Compliant Deposit: 11 December 2017
Date of Acceptance: 7 December 2017
Last Modified: 02 Jul 2019 12:46
URI: http://orca.cf.ac.uk/id/eprint/107496

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