RT Journal Article
SR Electronic
T1 Humans can navigate complex graph structures acquired during latent learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 723072
DO 10.1101/723072
A1 Milena Rmus
A1 Harrison Ritz
A1 Lindsay E Hunter
A1 Aaron M Bornstein
A1 Amitai Shenhav
YR 2021
UL http://biorxiv.org/content/early/2021/07/09/723072.abstract
AB Humans appear to represent many forms of knowledge in associative networks whose nodes are multiply connected, including sensory, spatial, and semantic. Recent work has shown that explicitly augmenting artificial agents with such graph-structured representations endows them with more human-like capabilities of compositionality and transfer learning. An open question is how humans acquire these representations. Previously, it has been shown that humans can learn to navigate graph-structured conceptual spaces on the basis of direct experience with trajectories that intentionally draw the network contours (Schapiro et al., 2012;2016), or through direct experience with rewards that covary with the underlying associative distance (Wu et al., 2018). Here, we provide initial evidence that this capability is more general, extending to learning to reason about shortest-path distances across a graph structure acquired across disjoint experiences with randomized edges of the graph - a form of latent learning. In other words, we show that humans can infer graph structures, assembling them from disordered experiences. We further show that the degree to which individuals learn to reason correctly and with reference to the structure of the graph corresponds to their propensity, in a separate task, to use model-based reinforcement learning to achieve rewards. This connection suggests that the correct acquisition of graph-structured relationships is a central ability underlying forward planning and reasoning, and may be a core computation across the many domains in which graph-based reasoning is advantageous.Competing Interest StatementThe authors have declared no competing interest.