Cognitive mechanisms of statistical learning and segmentation of continuous sensory input.
Two classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as pa...
| Publicado en: | Memory & Cognition Vol. 50; no. 5; pp. 979 - 997 |
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| Formato: | Artículo |
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Springer Nature
Jul2022
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| 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=157542317&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157542317 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Jul2022 vid: 50 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 157542317 10.3758/s13421-021-01264-0 ppf: 979 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: Cognitive mechanisms of statistical learning and segmentation of continuous sensory input. aug: au: Polyanskaya, Leona affil: Departamento de Psicología y Sociología, Universidad de Zaragoza, Teruel, Spain su: Semantics Recognition (Psychology) Cognition Language & languages Learning Visual perception Psychophysiology sug: subj: Semantics Recognition (Psychology) Cognition Language & languages Learning Visual perception Psychophysiology keyword: Artificial language Boundary-finding Clustering Sequence learning Statistical learning Word segmentation Artificial language Boundary-finding Clustering Sequence learning Statistical learning Word segmentation ab: Two classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as parts that form a single constituent. Bracketing (or boundary-finding) mechanisms work by identifying rarely co-occurring elements that correspond to the boundaries between discrete constituents. In a series of behavioral experiments, I tested which mechanisms are at play in the visual modality both during segmentation of a continuous syllabic sequence into discrete word-like constituents and during recognition of segmented constituents. Additionally, I explored conscious awareness of the products of statistical learning—whole constituents versus merged clusters of smaller subunits. My results suggest that both online segmentation and offline recognition of extracted constituents rely on detecting frequently co-occurring elements, a process likely based on associative memory. However, people are more aware of having learnt whole tokens than of recurrent composite clusters. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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