Predictive action tracking without motor experience in 8-month-old infants

Research output: Contribution to journalJournal articleResearchpeer-review


A popular idea in cognitive neuroscience is that to predict others’ actions, observers need to map those actions onto their own motor repertoire. If this is true, infants with a relatively limited motor repertoire should be unable to predict actions with which they have no previous motor experience. We investigated this idea by presenting pre-walking infants with videos of upright and inverted stepping actions that were briefly occluded from view, followed by either a correct (time-coherent) or an incorrect (time-incoherent) continuation of the action (Experiment 1). Pre-walking infants looked significantly longer to the still frame after the incorrect compared to the correct continuations of the upright, but not the inverted stepping actions. This demonstrates that motor experience is not necessary for predictive tracking of action kinematics. In a follow-up study (Experiment 2), we investigated sensorimotor cortex activation as a neural indication of predictive action tracking in another group of pre-walking infants. Infants showed significantly more sensorimotor cortex activation during the occlusion of the upright stepping actions that the infants in Experiment 1 could predictively track, than during the occlusion of the inverted stepping actions that the infants in Experiment 1 could not predictively track. Taken together, these findings are inconsistent with the idea that motor experience is necessary for the predictive tracking of action kinematics, and suggest that infants may be able to use their extensive experience with observing others’ actions to generate real-time action predictions.

Original languageEnglish
JournalBrain and Cognition
Pages (from-to)131-139
Number of pages9
Publication statusPublished - 2016

    Research areas

  • Action prediction, EEG, Infant development, Motor experience, Predictive action tracking, Sensorimotor alpha

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 179282564