Asymmetric reinforcement learning facilitates human inference of transitive relations.

ABSTRACT: Humans and other animals are capable of inferring never-experienced relations (for example, A > C) from other relational observations (for example, A > B and B > C). The processes behind such transitive inference are subject to intense research. Here we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across four experiments (n = 145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives

SUBMITTER: Ciranka S 

PROVIDER: S-EPMC9038534 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature