Optimal Control Methods for Simulating the Perception of Causality in Young Infants

There is a growing debate among developmental theorists concerning the perception of causality in young infants. Some theorists advocate a top-down view, e.g., that infants reason about causal events on the basis of intuitive physical principles. Others argue instead for a bottom-up view of infant causal knowledge, in which causal perception emerges from a simple set of associative learning rules. In order to test the limits of the bottom-up view, we propose an optimal control model (OCM) of infant causal perception. OCM is trained to find an optimal pattern of eye movements for maintaining sight of a target object. We first present a series of simulations which illustrate OCM’s ability to anticipate the outcome of novel, occluded causal events, and then compare OCM’s performance with that of 9-month-old infants. The implications for developmental theory and research are discussed.