What representations and computations underpin the contribution of the hippocampus to generalization and inference?

Empirical research and theoretical accounts have traditionally emphasized the function of the hippocampus in episodic memory. Here we draw attention to the importance of the hippocampus to generalization, and focus on the neural representations and computations that might underpin its role in tasks such as the paired associate inference (PAI) paradigm. We make a principal distinction between two different mechanisms by which the hippocampus may support generalization: an encoding-based mechanism that creates overlapping representations which capture higher-order relationships between different items [e.g., Temporal Context Model (TCM): Howard et al., 2005]—and a retrieval-based model [Recurrence with Episodic Memory Results in Generalization (REMERGE): Kumaran and McClelland, in press] that effectively computes these relationships at the point of retrieval, through a recurrent mechanism that allows the dynamic interaction of multiple pattern separated episodic codes. We also discuss what we refer to as transfer effects—a more abstract example of generalization that has also been linked to the function of the hippocampus. We consider how this phenomenon poses inherent challenges for models such as TCM and REMERGE, and outline the potential applicability of a separate class of models—hierarchical Bayesian models (HBMs) in this context. Our hope is that this article will provide a basic framework within which to consider the theoretical mechanisms underlying the role of the hippocampus in generalization, and at a minimum serve as a stimulus for future work addressing issues that go to the heart of the function of the hippocampus.