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.

Rescorla and Wagner's model of classical conditioning has been one of the most influential and successful theories of this fundamental learning process. The learning rule of their theory was first described as a learning procedure for connectionist networks by Widrow and Hoff. In this paper we propose a similar confluence of psychological and engineering constraints. Sutton has recently argued that adaptive prediction methods c