Proceedings of the Annual Meeting of the Cognitive Science Society

Human learning in complex environments critically dependson the ability to perform model selection, that is to assess com-peting hypotheses about the structure of the environment. Im-portantly, information is accumulated continuously, which ne-cessitates an online process for model selection. While modelselection in human learning has been explored extensively, it isunclear how memory systems support learning in an online set-ting. We formulate a semantic learner and demonstrate that on-line learning on open model spaces results in a delicate choicebetween either tracking a possibly infinite number of compet-ing models or retaining experiences in an intact form. Sincenone of these choices is feasible for a bounded-resource mem-ory system, we propose an episodic learner that retains an op-timised subset of experiences in addition to semantic memory.On a simple model system we demonstrate that this norma-tive theory of episodic memory can effectively circumvent thechallenge of online model selection.