The representational geometry of cognitive maps under dynamic cognitive control

Recent work has shown that the brain abstracts non-spatial relationships between entities or task states into representations called cognitive maps. Here, we investigate how cognitive control enables flexible top-down selection of goal-relevant information from multidimensional cognitive maps retrieved from memory. We examine the relationship between cognitive control and representational geometry by conducting parallel analyses of functional magnetic resonance imaging data and recurrent neural network models trained to perform the same task. We find both stable map-like representations in a medial temporal lobe and orbitofrontal cortical network that reflect both task-relevant and irrelevant dimensions and context-dependent, orthogonal representations of only task-relevant dimensions in a frontoparietal network. These representational motifs also emerge with distinct temporal profiles over the course of training in the recurrent neural network, with map-like representations appearing first. We further show that increasing control demands due to incongruence (conflicting responses) between current task-relevant and irrelevant dimensions impact the geometry of subjective representations, and the degree of this effect further accounts for individual differences in cognitive control. Taken together, our findings show how complementary representational geometries balance stability and behavioral flexibility, and reveal an intricate relationship between cognitive control and cognitive map geometry.