Recent findings suggest the hippocampal-entorhinal (HPC-ERC) system may serve a general mechanism for representing and navigating cognitive maps of non-spatial tasks. These map-like representations can be used to guide flexible goal-directed decisions. However, it is unclear whether this system, and interconnected ventromedial prefrontal cortex (vmPFC), use the same principles to guide everyday model-based decisions in the absence of continuous sensory feedback – such as whom to collaborate with or where to eat. In my talk I will present two fMRI studies designed to address these gaps.
In study 1 participants first separately learned the rank of neighboring people on each of two social dimensions–popularity and competence–in two separate groups. Participants could infer the overall structure of each group hierarchy, which was never shown, through transitive inferences. Next, they learned the relative rank of select individuals (“hubs”) in each group with the other group, creating a unique associative path between groups. Finally, they made inferences about the rank of novel pairs between the two groups. Reaction times and BOLD activity in ERC and vmPFC during decisions depended on the Euclidian distance to the latent hub. FMRI suppression further revealed the latent hub was reinstated in HPC during decisions to guide inferences. Finally, pattern similarity analyses showed that people who were more proximal in Euclidian distance in the mentally reconstructed 2-D space were represented progressively more similarly in HPC and ERC, consistent with a cognitive map of the abstract 2-D social hierarchy. These results shed light on how abstract and discrete structures are represented, navigated, and combined in the human brain.
In study 2 we investigated the neural code that might underpin discrete decisions about individuals in the social network. In each trial, an entrepreneur was presented with two potential collaborators. Participants were asked to choose the better partner between two for a given entrepreneur by comparing the ‘growth potential (GP)’ of two pairs. First, we replicated the finding that the level of pattern dissimilarity in the HPC and ERC increased with the pairwise Euclidean distance between entrepreneurs in the 2-D social network, suggesting that separately learned dimensions are integrated into a 2-D cognitive map. Second, we found that the ERC, vmPFC, intraparietal area, and posteromedial cortex all display hexadirectional signals to encode the direction of trajectories between entrepreneurs over the abstract social space. Finally, we found that the ERC and vmPFC encode the difference in GP between pairs at the time of decisions, suggesting their roles in comparing the values of each decision option from the multidimensional cognitive map. Our findings show that a grid-like code in the human brain is extended to encode trajectories over an abstract and discrete social space during decision making, which may suggest a general mechanism for how the brain implements model-based decisions.