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Autor/inn/enRadulescu, Silvia; Wijnen, Frank; Avrutin, Sergey
TitelPatterns Bit by Bit. An Entropy Model for Rule Induction
QuelleIn: Language Learning and Development, 16 (2020) 2, S.109-140 (32 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1547-5441
DOI10.1080/15475441.2019.1695620
SchlagwörterLinguistic Input; Language Acquisition; Grammar; Learning Processes; Language Processing; Models; Memorization; Prediction; Psycholinguistics; Inferences; Artificial Languages; Generalization; Infants; Adults; Syllables; Indo European Languages; Auditory Stimuli; Task Analysis; Familiarity
AbstractFrom limited evidence, children track the regularities of their language impressively fast and they infer generalized rules that apply to novel instances. This study investigated what drives the inductive leap from memorizing specific items and statistical regularities to extracting abstract rules. We propose an innovative entropy model that offers one consistent information-theoretic account for both learning the regularities in the input and generalizing to new input. The model predicts that rule induction is an encoding mechanism gradually driven as a natural automatic reaction by the brain's sensitivity to the input complexity (entropy) interacting with the finite encoding power of the human brain (channel capacity). In two artificial grammar experiments with adults we probed the effect of input complexity on rule induction. Results showed that as the input becomes more complex, the tendency to infer abstract rules increases gradually. (As Provided).
AnmerkungenRoutledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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