Dissociating spatial and cognitive grid representations in the brain

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Humans and primates display an impressive knack for acquiring knowledge in one environment (for example the arrangement of produce and dairy at the grocery store) and rapidly applying it in a completely novel one (a new grocery store). The ability to generalize behavior from past contexts to new ones is critical to prediction, inference, and planning in the real world. Though models of artificial intelligence inspired by neural learning algorithms have achieved human levels of performance on a wide range of tasks, the most prominent such models are incapable of generalizing information beyond the problems that they were trained on. In principle, such transfer requires extracting abstract information from one situation and then applying it in another, yet the neural mechanisms of this abstraction process remain unknown. Here we propose to test the hypothesis that the brain achieves such transfer using an abstract cognitive map, much like that used to navigate physical space. In particular, we propose to test whether grid-like representations in the brain, which are thought to facilitate spatial navigation and can be observed in neural firing or fMRI BOLD activity, tile an abstract space (eg. the layout of a typical grocery store) and facilitate the transfer of information collected in one environment (eg. Whole Foods) to another (eg. Eastside Market). To do so, we developed a novel task in which abstract grid representations can be dissociated through neuroimaging from more standard spatial grid representations. We will test which of these patterns is observed in the BOLD activity of the entorhinal cortex concurrent with task performance, and the degree to which such patterns relate to behavioral measures of transfer learning. We will incorporate results from these analyses into a model that learns to associate cues with locations via grid-like basis functions. The results of this study will shed light on the neural mechanisms of transfer learning and serve as preliminary data for an R01 application focused on neural representations for efficient learning.