Data augmentation as a framework for modeling hippocampal contributions to generalization

Abstract

The hippocampus plays a critical role in generalization, enabling us to flexibly repurpose prior experiences to perform novel tasks. Here we suggest that data augmentation---a machine learning strategy to improve generalization by refactoring prior experience---offers a useful framework to conceptualize and model hippocampal function. We begin by outlining how data augmentation operates across two timescales: the traditional ``offline'' setting, where refactoring training data yields more general representations, and an ``online'' setting, where retrieved experiences can be flexibly refactored at test time to support zero-shot inference. We suggest that these `offline' and `online' computational strategies map onto functions supported by the hippocampus. Critically, we argue that these computational tools can be leveraged to develop formal `linking functions' between experimental evidence and theoretical claims, such that a unified modeling approach can be used to predict the diverse behaviors that depend on the hippocampus---from navigating in high-dimensional sensory environments to more abstract inferences. We hope this perspective, and the modeling strategies it makes available, will support new efforts to formalize and evaluate theories of hippocampal function.