Individual differences in distributional statistical learning: Better frequency "discriminators" are better "estimators".
People can easily extract and encode statistical information from their environment. However, research has primarily focused on conditional statistical learning (i.e., the ability to learn joint and conditional relationships between stimuli) and has largely neglected distributional statistical learn...
| Publicado en: | Quarterly Journal of Experimental Psychology Vol. 78; no. 9; pp. 1921 - 1932 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Sep2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187242792&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187242792 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 17470218 1US5 jtl: Quarterly Journal of Experimental Psychology issn: 17470218 maglogo: Y pubinfo: dt: Sep2025 vid: 78 iid: 9 pid: 344 pub: Sage Publications Inc. artinfo: ui: 187242792 10.1177/17470218241293235 ppf: 1921 ppct: 11 formats: tig: atl: Individual differences in distributional statistical learning: Better frequency "discriminators" are better "estimators". aug: au: Growns, Bethany Martire, Kristy A Mattijssen, Erwin J A T affil: School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand School of Psychology, University of New South Wales, Sydney, NSW, Australia Behavioural Science Institute, Radboud University Nijmegen & The Netherlands Forensic Institute, Den Haag, The Netherlands su: Psychometrics Individual differences Statistical learning Signal frequency estimation Pattern perception Associative learning sug: subj: Psychometrics Individual differences Statistical learning Signal frequency estimation Pattern perception Associative learning keyword: distributional learning individual differences psychometrics distributional learning individual differences psychometrics ab: People can easily extract and encode statistical information from their environment. However, research has primarily focused on conditional statistical learning (i.e., the ability to learn joint and conditional relationships between stimuli) and has largely neglected distributional statistical learning (i.e., the ability to learn the frequency and variability of distributions). For example, learning that "E" is more common in the English alphabet than "Z." In this article, we investigate how distributional learning can be measured by exploring the relationship between, and psychometric properties of, four different measures of distributional learning—from the ability to discriminate relative frequencies to the ability to estimate frequencies. We identified moderate relationships between four distributional learning measures and these tasks accounted for a substantial portion of the variance in performance across tasks (44.3%). A measure of divergent validity (intrinsic motivation) did not significantly correlate with any statistical learning measure and accounted for a separate portion of the variance across tasks. Our results suggest that distributional statistical learning encompasses the ability to discriminate between relative frequencies and estimating them. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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