A mathematical theory of relational generalization in transitive inference.

ABSTRACT: Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. Here we investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation (A > B and B > C) and generalize it to new combinations of items (A > C). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signa

SUBMITTER: Lippl S 

PROVIDER: S-EPMC10473627 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature