Feature distribution learning by passive exposure.


Journal

Cognition
ISSN: 1873-7838
Titre abrégé: Cognition
Pays: Netherlands
ID NLM: 0367541

Informations de publication

Date de publication:
10 2022
Historique:
received: 04 11 2021
revised: 20 06 2022
accepted: 21 06 2022
pubmed: 6 7 2022
medline: 4 8 2022
entrez: 5 7 2022
Statut: ppublish

Résumé

Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects of feature distributions. In FDL, observers learn the full shape of a distribution of features in a set of distractor stimuli and use this information to improve visual search: response times (RT) are slowed if the target feature lies inside the previous distractor distribution, and the RT patterns closely reflect the distribution shape. FDL requires only a few trials and is markedly sensitive to different distribution types. It is unknown, however, whether our perceptual system encodes feature distributions automatically and by passive exposure, or whether this learning requires active engagement with the stimuli. In two experiments, we sought to answer this question. During an initial exposure stage, participants passively viewed a display of 36 lines that included one orientation singleton or no singletons. In the following search display, they had to find an oddly oriented target. The orientations of the lines were determined either by a Gaussian or a uniform distribution. We found evidence for FDL only when the passive trials contained an orientation singleton. Under these conditions, RT's decreased as a function of the orientation distance between the target and the mean of the exposed distractor distribution. These results suggest that passive exposure to a distribution of visual features can affect subsequent search performance, but only if a singleton appears during exposure to the distribution.

Identifiants

pubmed: 35785655
pii: S0010-0277(22)00199-8
doi: 10.1016/j.cognition.2022.105211
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

105211

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

David Pascucci (D)

Laboratory of Psychophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Electronic address: david.pascucci@epfl.ch.

Gizay Ceylan (G)

Laboratory of Psychophysics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.

Árni Kristjánsson (Á)

Vision Sciences Laboratory, School of Health Sciences, University of Iceland, Reykjavik, Iceland.

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Classifications MeSH