A goal-centric outlook on learning.
abstraction
computational modeling
decision-making
goals
learning
motivation
reinforcement learning
rewards
Journal
Trends in cognitive sciences
ISSN: 1879-307X
Titre abrégé: Trends Cogn Sci
Pays: England
ID NLM: 9708669
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
13
06
2023
revised:
11
08
2023
accepted:
14
08
2023
medline:
17
11
2023
pubmed:
12
9
2023
entrez:
11
9
2023
Statut:
ppublish
Résumé
Goals play a central role in human cognition. However, computational theories of learning and decision-making often take goals as given. Here, we review key empirical findings showing that goals shape the representations of inputs, responses, and outcomes, such that setting a goal crucially influences the central aspects of any learning process: states, actions, and rewards. We thus argue that studying goal selection is essential to advance our understanding of learning. By following existing literature in framing goal selection within a hierarchy of decision-making problems, we synthesize important findings on the principles underlying goal value attribution and exploration strategies. Ultimately, we propose that a goal-centric perspective will help develop more complete accounts of learning in both biological and artificial agents.
Identifiants
pubmed: 37696690
pii: S1364-6613(23)00207-3
doi: 10.1016/j.tics.2023.08.011
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1150-1164Informations de copyright
Copyright © 2023 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests No interests are declared.