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.
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