Prospective search time estimates reveal the strengths and limits of internal models of visual search.


Journal

Journal of experimental psychology. General
ISSN: 1939-2222
Titre abrégé: J Exp Psychol Gen
Pays: United States
ID NLM: 7502587

Informations de publication

Date de publication:
Jul 2023
Historique:
medline: 26 6 2023
pubmed: 21 3 2023
entrez: 20 3 2023
Statut: ppublish

Résumé

Having an internal model of one's attention can be useful for effectively managing limited perceptual and cognitive resources. While previous work has hinted at the existence of an internal model of attention, it is still unknown how rich and flexible this model is, whether it corresponds to one's own attention or to a generic person-invariant schema, and whether it is specified as a list of facts and rules or alternatively as a probabilistic simulation model. To this end, we tested participants' ability to estimate their own behavior in a visual search task with novel displays. In six online experiments (four pre-registered), prospective search time estimates reflected accurate metacognitive knowledge of key findings in the visual search literature, including the set-size effect, higher efficiency of color over conjunction search, and the asymmetric contributions of target and distractor identities to search difficulty. In contrast, estimates were biased to assume serial search, and demonstrated little to no insight into sizeable effects of search asymmetries for basic visual features, and of target-distractor similarity. Together, our findings reveal a complex picture, where internal models of visual search are sensitive to some, but not all, of the factors that make some searches more difficult than others. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Identifiants

pubmed: 36939608
pii: 2023-55287-001
doi: 10.1037/xge0001360
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1951-1966

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom

Auteurs

Matan Mazor (M)

Wellcome Centre for Human Neuroimaging, University College London.

Max Siegel (M)

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.

Joshua B Tenenbaum (JB)

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.

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Classifications MeSH