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Mechanisms of developmental change in infant categorization

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Mechanisms of developmental change in infant categorization. / Westermann, Gert; Mareschal, Denis.
In: Cognitive Development, Vol. 27, No. 4, 10.2012, p. 367-382.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Westermann, G & Mareschal, D 2012, 'Mechanisms of developmental change in infant categorization', Cognitive Development, vol. 27, no. 4, pp. 367-382. https://doi.org/10.1016/j.cogdev.2012.08.004

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Westermann G, Mareschal D. Mechanisms of developmental change in infant categorization. Cognitive Development. 2012 Oct;27(4):367-382. doi: 10.1016/j.cogdev.2012.08.004

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Westermann, Gert ; Mareschal, Denis. / Mechanisms of developmental change in infant categorization. In: Cognitive Development. 2012 ; Vol. 27, No. 4. pp. 367-382.

Bibtex

@article{ede3a213dc284771a55986e83dacf093,
title = "Mechanisms of developmental change in infant categorization",
abstract = "Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development.",
keywords = "Infancy, Categorization , Learning, Development , Mechanism , Connectionist modeling",
author = "Gert Westermann and Denis Mareschal",
year = "2012",
month = oct,
doi = "10.1016/j.cogdev.2012.08.004",
language = "English",
volume = "27",
pages = "367--382",
journal = "Cognitive Development",
issn = "0885-2014",
publisher = "Elsevier Limited",
number = "4",

}

RIS

TY - JOUR

T1 - Mechanisms of developmental change in infant categorization

AU - Westermann, Gert

AU - Mareschal, Denis

PY - 2012/10

Y1 - 2012/10

N2 - Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development.

AB - Computational models are tools for testing mechanistic theories of learning and development. Formal models allow us to instantiate theories of cognitive development in computer simulations. Model behavior can then be compared to real performance. Connectionist models, loosely based on neural information processing, have been successful in capturing a range of developmental phenomena, in particular on-line within-task category learning by young infants. Here we describe two new models. One demonstrates how age dependent changes in neural receptive field sizes can explain observed changes in on-line category learning between 3 and 10 months of age. The other aims to reconcile two conflicting views of infant categorization by focusing on the different task requirements of preferential looking and manual exploration studies. A dual-memory hypothesis posits that within-task category learning that drives looking time behaviors is based on a fast-learning memory system, whereas categorization based on background experience and assessed by paradigms requiring complex motor behavior relies on a second, slow-learning system. The models demonstrate how emphasizing the mechanistic causes of behaviors leads to discovery of deeper, more explanatory accounts of learning and development.

KW - Infancy

KW - Categorization

KW - Learning

KW - Development

KW - Mechanism

KW - Connectionist modeling

UR - http://www.scopus.com/inward/record.url?scp=84869886964&partnerID=8YFLogxK

U2 - 10.1016/j.cogdev.2012.08.004

DO - 10.1016/j.cogdev.2012.08.004

M3 - Journal article

AN - SCOPUS:84869886964

VL - 27

SP - 367

EP - 382

JO - Cognitive Development

JF - Cognitive Development

SN - 0885-2014

IS - 4

ER -