Perceptual intake explains variability in statistical word segmentation.

Artificial language Domain specificity Linguistic entrenchment Rhythm perception Statistical learning

Journal

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

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 12 04 2023
revised: 04 08 2023
accepted: 03 09 2023
pubmed: 23 9 2023
medline: 23 9 2023
entrez: 22 9 2023
Statut: ppublish

Résumé

One of the first problems in language learning is to segment words from continuous speech. Both prosodic and distributional information can be useful, and it is an important question how the two types of information are integrated. In this paper, we propose that the distinction between input (the statistical properties of the syllable sequence), and intake (how learners perceptually represent the syllable sequence) is a useful framework to integrate different sources of information. We took a novel approach, observing how a large number of syllable sequences were segmented. These sequences had the same transitional probability information for finding word boundaries but different syllables in them. We found large variability in the performance of the segmentation task, suggesting that factors other than the statistical properties of sequences were at play. This variability was explored using the input/intake asymmetry framework, which predicted that factors that shaped the representation of different syllable sequences could explain the variability of learning. We examined two factors, the saliency of the rhythm in these syllable sequences and how familiar the novel word forms in the sequence were to the existing lexicon. Both factors explained the variance in the learnability of different sequences, suggesting that processing of the sequences shaped learning. The implications of these results to computational models of statistical learning and broader implications to language learning were discussed.

Identifiants

pubmed: 37738711
pii: S0010-0277(23)00246-9
doi: 10.1016/j.cognition.2023.105612
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105612

Informations de copyright

Copyright © 2023. Published by Elsevier B.V.

Auteurs

Felix Hao Wang (FH)

School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China. Electronic address: haowang1@sas.upenn.edu.

Meili Luo (M)

School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.

Suiping Wang (S)

Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China. Electronic address: wangsuiping@m.scnu.edu.cn.

Classifications MeSH