Hippocampal spatio-temporal cognitive maps adaptively guide reward generalization
Garvert, M. M.; Saanum, T.; Schulz, E.; Schuck, N. W.; Doeller, C. F.
Show abstract
The brain forms cognitive maps of relational knowledge, an organizing principle thought to underlie our ability to generalize and make inferences. However, how can a relevant map be selected in situations where a stimulus is embedded in multiple relational structures? Here, we find that both spatial and temporal cognitive maps influence generalization in a choice task, where spatial location determines reward magnitude. Mirroring behavior, the hippocampus not only builds a map of spatial relationships but also encodes temporal distances. As the task progresses, participants choices become more influenced by spatial relationships, reflected in a strengthening of the spatial and a weakening of the temporal map. This change is driven by orbitofrontal cortex, which represents the evidence that an observed outcome is generated from the spatial rather than the temporal map and updates hippocampal representations accordingly. Taken together, this demonstrates how hippocampal cognitive maps are used and updated flexibly for inference.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Human hippocampus and dorsomedial prefrontal cortex infer and update latent causes during social interaction 98%
- Joint representation of working memory and uncertainty in human cortex 98%
- Entorhinal and ventromedial prefrontal cortices abstract and generalise the structure of reinforcement learning problems 97%
Similar papers in this journal
- Neural evidence for boundary updating as the source of the repulsive bias in classification 97%
- Spatial processing of limbs reveals the center-periphery bias in high level visual cortex follows a nonlinear topography 97%
- Dissociable contributions of the medial parietal cortex to recognition memory 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.