Episodic memory as a prerequisite for online updates of model structure


Human learning in complex environments critically depends on the ability to perform model selection, that is to assess competing hypotheses about the structure of the environment. Importantly, information is accumulated continuously, which necessitates an online process for model selection. While model selection in human learning has been explored extensively, it is unclear how memory systems support learning in an online setting. We formulate a semantic learner and demonstrate that online learning on open model spaces results in a delicate choice between either tracking a possibly infinite number of competing models or retaining experiences in an intact form. Since none of these choices is feasible for a bounded-resource memory system, we propose an episodic learner that retains an optimised subset of experiences in addition to semantic memory. On a simple model system we demonstrate that this normative theory of episodic memory can effectively circumvent the challenge of online model selection.

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