Identifying Transfer Learning in the Reshaping of Inductive Biases.

ABSTRACT: Transfer learning, the reuse of newly acquired knowledge under novel circumstances, is a critical hallmark of human intelligence that has frequently been pitted against the capacities of artificial learning agents. Yet, the computations relevant to transfer learning have been little investigated in humans. The benefit of efficient inductive biases (meta-level constraints that shape learning, often referred as priors in the Bayesian learning approach), has been both theoretically and experimentally established. Efficiency of inductive biases depends on their capacity to generalize earlier experiences. We argue that successful transfer learning upon task acquisition is ensured by updating inductive biases and transfer of knowledge hinges upon capturing the structure of the task in the induct

SUBMITTER: Szekely A 

PROVIDER: S-EPMC11410354 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature