Computational Models of Episodic Memory

Computational models of episodic memory provide tools to better understand the latent neurocognitive processes underlying retention of information about specific events from one's life. This chapter discusses the representations, associations, and dynamics of influential models of episodic memory, with particular emphasis on models of recognition and free recall tasks. In-depth discussion and model-fitting results of four models – the retrieving effectively from memory (REM) model, the bind cue decide model of episodic memory (BCDMEM), the search of associative memory (SAM) model, and the temporal context model (TCM) – are provided to facilitate understanding of these models, as well as similarities and differences between them. Alternative modeling frameworks, including neural network models, are discussed. Throughout, the importance of context in models of episodic memory is emphasized, particularly for free recall tasks.