Flexible neural representations of abstract structural knowledge in the human entorhinal cortex.

Humans' ability for generalization is outstanding. It is flexible enough to identify cases where knowledge from prior tasks is relevant, even when many features of the current task are different, such as the sensory stimuli or the size of the task state space. We have previously shown that in abstract tasks, humans can generalize knowledge in cases where the only cross-task shared feature is the statistical rules that govern the task's state-state relationships. Here, we hypothesized that this capacity is associated with generalizable representations in the entorhinal cortex (EC). This hypothesis was based on the EC's generalizable representations in spatial tasks and recent discoveries about its role in the representation of abstract tasks. We first develop an analysis method capable of testing for such representations in fMRI data, explain why other common methods would have failed for our task, and validate our method through a combination of electrophysiological data analysis, simulations, and fMRI sanity checks. We then show with fMRI that EC representations generalize across complex non-spatial tasks that share a hexagonal grid structural form but differ in their size and sensory stimuli, that is their only shared feature is the rules governing their statistical structure. There was no clear evidence for such generalization in EC for non-spatial tasks with clustered, as opposed to planar, structure.