Statistics in motion: Does the infant motor system predict actions based on their transitional probability?
Motor theories of action prediction propose that our neural motor system combines prior knowledge about likely action outcomes with current sensory input to predict other people's behavior. This knowledge can be acquired through observational experience, more specifically statistical learning. Recently, it has been shown that infants can detect in a stream of actions two actions that follow each other deterministically and that their motor system uses this knowledge to predict upcoming actions. However, real-life actions are more complex: whereas actions hardly ever follow one another with 100% probability, often certain actions are more likely to follow one another than others (e.g., grasping a mug to drink versus to pass it to someone). Here, we examined whether infants can learn the statistical structure of action sequences through observation and whether the activity of their motor system reflects the specific statistical likelihood of upcoming actions. We trained 18-month-old infants at home with videos of action sequences featuring different transitional probabilities. At test, motor activity was measured using EEG during perceptually identical time windows that linked actions with four probability levels (100%, 75%, 50%, 25%). We found that motor activity was parametrically modulated by the transitional probability of action pairs. Specifically, our results showed the strongest predictive motor activity for deterministic actions and least activity for actions with low levels of probability. These results show that infants’ predictive motor activity reflects the specific statistical likelihood of upcoming actions and thus underline the important role of statistical learning for infants’ developing action understanding.
- EEG: preprocesing scripts for the EEG data using Fieldtrip
- Analysis: R scripts to run linear mixed-effect models on the extracted frequency power values