Consciousness influences the enhancement of visual statistical learning in Zipfian distributions.


Journal

Journal of experimental psychology. Learning, memory, and cognition
ISSN: 1939-1285
Titre abrégé: J Exp Psychol Learn Mem Cogn
Pays: United States
ID NLM: 8207540

Informations de publication

Date de publication:
26 Oct 2023
Historique:
medline: 26 10 2023
pubmed: 26 10 2023
entrez: 26 10 2023
Statut: aheadofprint

Résumé

It has been reported that visual statistical learning (VSL) is facilitated in skewed distributions. However, it remains unclear whether enhancement of VSL in Zipfian distributions is due to consciousness of the regularities presented at high frequency. This study addressed this issue. We measured participants' subjective confidence in regularities and awareness of regularities during familiarization by combining a previously reported procedure for VSL with a postdecision wagering task and posttest questionnaire. The results demonstrated that Zipfian distribution enhanced not only VSL but also metacognitive sensitivity, particularly for high-frequency regularities, as the effects of consciousness on VSL were limited to high-frequency regularities. Moreover, the results indicated that awareness during familiarization mediated VSL enhancement in the Zipfian distribution. These results suggest that VSL for events with high-frequency regularities plays an important role in the cognition of events with low-frequency regularities via awareness. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Identifiants

pubmed: 37883051
pii: 2024-20454-001
doi: 10.1037/xlm0001275
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Japan Society for the Promotion of Science

Auteurs

Sachio Otsuka (S)

Graduate School of Human and Environmental Studies, Kyoto University.

Yuki Miura (Y)

Faculty of Integrated Human Studies, Kyoto University.

Jun Saiki (J)

Graduate School of Human and Environmental Studies, Kyoto University.

Classifications MeSH