Fabian Renz | Representation learning facilitates different levels of generalization

Cognitive maps represent relational structures and are taken to be important for

generalization and optimal decision-making in spatial as well as non-spatial domains.

While many studies have investigated the benefits of cognitive maps, how these

maps are learned has remained less clear. We introduce a new graph-structured

sequence task to better understand how cognitive maps are learned. Participants

observed sequences of episodes followed by a reward, thereby learning about the

underlying transition structure and fluctuating reward contingencies. Importantly, the

task structure allowed participants to generalize value from some episode sequences

to others. Behavioral data demonstrated participants` ability to generalize value to

unobserved episodes in the map. We hypothesize that a possible neural mechanism

involved in learning cognitive maps and leveraging afforded generalizations as

described here is experience replay.