Monroy, Claire, Meyer, Marlene, Schroer, Lisanne, Gerson, Sarah ORCID: https://orcid.org/0000-0001-8710-1178 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 |
Preview |
PDF
- Accepted Post-Print Version
Download (678kB) | Preview |
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: | 06 Nov 2023 21:43 |
URI: | https://orca.cardiff.ac.uk/id/eprint/107496 |
Citation Data
Cited 16 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
Edit Item |