Daniel McNamee

The paper shows how motor and mnemonic systems interact to form sensorimotor cognitive maps during movement learning, with individual differences shaping these maps.

This paper introduces a computational theory called topology-aware grid coding (TAG) that enables rapid generalization of spatial navigation in environments with the same topological structure but different geometries.

This study introduces time-magnitude RL (TMRL), a new reinforcement learning framework, and shows that dopamine neurons encode a probabilistic map of future rewards in mice.

This study examines how expert London taxi drivers plan routes by precaching decisions at key junctions, using complexity metrics to prioritize mental sampling during route planning.

This study shows that London taxi drivers use hierarchical planning by organizing city regions into clusters, leading to faster route recall compared to distance-based planning.

The paper investigates distinct neural replay signatures during decision-making and memory preservation in humans using MEG and multivariate analysis.