Document Detail

Rules versus statistics: insights from a highly inflected language.
MedLine Citation:
PMID:  21564267     Owner:  NLM     Status:  MEDLINE    
Inflectional morphology has been taken as a paradigmatic example of rule-governed grammatical knowledge (Pinker, 1999). The plausibility of this claim may be related to the fact that it is mainly based on studies of English, which has a very simple inflectional system. We examined the representation of inflectional morphology in Serbian, which encodes number, gender, and case for nouns. Linguists standardly characterize this system as a complex set of rules, with disagreements about their exact form. We present analyses of a large corpus of nouns which showed that, as in English, Serbian inflectional morphology is quasiregular: It exhibits numerous partial regularities creating neighborhoods that vary in size and consistency. We then asked whether a simple connectionist network could encode this statistical information in a manner that also supported generalization. A network trained on 3,244 Serbian nouns learned to produce correctly inflected phonological forms from a specification of a word's lemma, gender, number, and case, and generalized to untrained cases. The model's performance was sensitive to variables that also influence human performance, including surface and lemma frequency. It was also influenced by inflectional neighborhood size, a novel measure of the consistency of meaning to form mapping. A word-naming experiment with native Serbian speakers showed that this measure also affects human performance. The results suggest that, as in English, generating correctly inflected forms involves satisfying a small number of simultaneous probabilistic constraints relating form and meaning. Thus, common computational mechanisms may govern the representation and use of inflectional information across typologically diverse languages.
Jelena Mirković; Mark S Seidenberg; Marc F Joanisse
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Cognitive science     Volume:  35     ISSN:  1551-6709     ISO Abbreviation:  Cogn Sci     Publication Date:    2011 May-Jun
Date Detail:
Created Date:  2011-05-13     Completed Date:  2011-09-26     Revised Date:  2013-06-30    
Medline Journal Info:
Nlm Unique ID:  7708195     Medline TA:  Cogn Sci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  638-81     Citation Subset:  IM    
Copyright Information:
Copyright © 2011 Cognitive Science Society, Inc.
Department of Psychology, University of York, UK.
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MeSH Terms
Language Development
Neural Networks (Computer)*
Grant Support
R01 MH058723-05/MH/NIMH NIH HHS; R01-MH58723/MH/NIMH NIH HHS; //Canadian Institutes of Health Research

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