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 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: actions rarely follow one another with 100% probability, certain actions are more likely to follow one another than others. 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 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 identical time windows that linked actions with four probability levels. While the planned analysis did not reveal a significant effect of probability, an exploratory analysis revealed that infants' motor activity over the left-central hemisphere showed a linear relationship with transitional probability of action pairs specifically in the beginning of the experiment, when infants were not fatigued, yet. Predictive motor activity was strongest when the probability was highest and weakest when no prediction was involved. These results suggest that infants' motor system is sensitive to the statistical likelihood of upcoming actions and underline the important role of statistical learning for infants' developing action understanding.