Fig. 6: Policy transfer: learning graphs with underlying community structure.
From: Transferring structural knowledge across cognitive maps in humans and models
a Our agent was able to infer the correct number of communities (middle panel, averaged inferred number of communities over 20 simulations). It was also able to infer the identities of the connecting nodes (lower panel, inferred number of nodes divided to the number of connecting nodes according to the inferred graph size, see ‘Methods’). Shadows are the SEM, the centre is the mean. b Participants with correct structural prior spend less time on learning the associations between the pictures (RT = response time for changing to the next pair, upper panel—left). The number of steps to the target (nsteps) is significantly lower for participants with the correct structural prior (upper panels, Dt=0 is the initial number of links between the current picture and the target). During navigation, participants with the correct prior over the structural forms choose connecting nodes more frequently (lower panel—left), they do so even if this choice takes them far away from the target (lower panel—right). Error bars are the SEM, the centre is the mean. *p < 0.05, **p < 0.01 (20 participants in each group).
