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Toward a connectionist model of recursion in human linguistic performance

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Christiansen, Morten H. and Chater, Nick (1999) Toward a connectionist model of recursion in human linguistic performance. Cognitive Science, 23 (2). pp. 157-205. doi:10.1207/s15516709cog2302_2

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Official URL: http://dx.doi.org/10.1207/s15516709cog2302_2

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Abstract

Naturally occurring speech contains only a limited amount of complex recursive structure, and this is reflected in the empirically documented difficulties that people experience when processing such structures. We present a connectionist model of human performance in processing recursive language structures. The model is trained on simple artificial languages. We find that the qualitative performance profile of the model matches human behavior, both on the relative difficulty of center-embedding and cross-dependency, and between the processing of these complex recursive structures and right-branching recursive constructions; We analyze how these differences in performance are reflected in the internal representations of the model by performing discriminant analyses on these representations both before and after training. Furthermore, we show how a network trained to process recursive structures can also generate such structures in a probabilistic fashion. This work suggests a novel explanation of people's limited recursive performance, without assuming the existence of a mentally represented competence grammar allowing unbounded recursion.

Item Type: Journal Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Divisions: Faculty of Social Sciences > Warwick Business School > Behavioural Science
Faculty of Social Sciences > Warwick Business School
Journal or Publication Title: Cognitive Science
Publisher: Psychology Press
ISSN: 0364-0213
Official Date: April 1999
Dates:
DateEvent
April 1999UNSPECIFIED
Volume: 23
Number: 2
Number of Pages: 49
Page Range: pp. 157-205
DOI: 10.1207/s15516709cog2302_2
Status: Peer Reviewed
Publication Status: Published

Data sourced from Thomson Reuters' Web of Knowledge

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