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.

The current eye-tracking study investigated whether toddlers use statistical information to make anticipatory eye movements while observing continuous action sequences. In two conditions, 19-month-old participants watched either a person performing an action sequence (Agent condition) or a self-propelled visual event sequence (Ghost condition). Both sequences featured a statistical structure in which certain action pairs occurred with deterministic transitional probabilities. Toddlers learned the transitional probabilities between the action steps of the deterministic action pairs and made predictive fixations to the location of the next action in the Agent condition but not in the Ghost condition. These findings suggest that young toddlers gain unique information from the statistical structure contained within action sequences and are able to successfully predict upcoming action steps based on this acquired knowledge. Furthermore, predictive gaze behavior was correlated with reproduction of sequential actions following exposure to statistical regularities. This study extends previous developmental work by showing that statistical learning can guide the emergence of anticipatory eye movements during observation of continuous action sequences.

Infants are sensitive to structure and patterns within continuous streams of sensory input. This sensitivity relies on statistical learning, the ability to detect predictable regularities in spatial and temporal sequences. Recent evidence has shown that infants can detect statistical regularities in action sequences they observe, but little is known about the neural process that give rise to this ability. In the current experiment, we combined electroencephalography (EEG) with eye-tracking to identify electrophysiological markers that indicate whether 8-11-month-old infants detect violations to learned regularities in action sequences, and to relate these markers to behavioral measures of anticipation during learning. In a learning phase, infants observed an actor performing a sequence featuring two deterministic pairs embedded within an otherwise random sequence. Thus, the first action of each pair was predictive of what would occur next. One of the pairs caused an action-effect, whereas the second did not. In a subsequent test phase, infants observed another sequence that included deviant pairs, violating the previously observed action pairs. Event-related potential (ERP) responses were analyzed and compared between the deviant and the original action pairs. Findings reveal that infants demonstrated a greater Negative central (Nc) ERP response to the deviant actions for the pair that caused the action-effect, which was consistent with their visual anticipations during the learning phase. Findings are discussed in terms of the neural and behavioral processes underlying perception and learning of structured action sequences.