Székely, Anna, Török, Balázs, Kiss, Mariann, Janacsek, Karolina ORCID: https://orcid.org/0000-0001-7829-8220, Németh, Dezső and Orbán, Gergő (2024) Identifying transfer learning in the reshaping of inductive biases. Open Mind, 8. pp. 1107-1128. ISSN 2470-2986 (Online) (doi:10.1162/opmi_a_00158)

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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 inductive bias that can be reused in novel tasks. To explore this, we trained participants on a non-trivial visual stimulus sequence task (Alternating Serial Response Times, ASRT); during the Training phase, participants were expo

Item Type:	Article
Additional Information:	In Special Collection: CogNet.
Uncontrolled Keywords:	statistical learning, transfer learning, internal model, computational modeling, Bayesian learning, inductive bias
Subjects:	B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > H Social Sciences (General)
Faculty / School / Research Centre / Research Group:	Faculty of Education, Health & Human Sciences Faculty of Education, Health & Human Sciences > Institute for Lifecourse Development Faculty of Education, Health & Human Sciences > Institute for Lifecourse Development > Centre for Thinking and Learning Faculty of Education, Health & Human Sciences > School of Human Sciences (HUM)
Last Modified:	08 Oct 2024 15:05
URI:	http://gala.gre.ac.uk/id/eprint/48261

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