Habituation Reflects Optimal Exploration Over Noisy Perceptual Samples.
Bayesian modeling
Cognitive development
Decision-making
Learning
Journal
Topics in cognitive science
ISSN: 1756-8765
Titre abrégé: Top Cogn Sci
Pays: United States
ID NLM: 101506764
Informations de publication
Date de publication:
04 2023
04 2023
Historique:
revised:
14
10
2022
received:
15
09
2022
accepted:
14
10
2022
medline:
25
4
2023
pubmed:
3
11
2022
entrez:
2
11
2022
Statut:
ppublish
Résumé
From birth, humans constantly make decisions about what to look at and for how long. Yet, the mechanism behind such decision-making remains poorly understood. Here, we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on expected information gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that (1) learning and perceptual noise are critical assumptions of the model, and (2) Surprisal and KL are good proxies for EIG under the current learning context.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
290-302Informations de copyright
© 2022 Cognitive Science Society LLC.
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