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Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing
Author(s) -
Devereux Barry J.,
Taylor Kirsten I.,
Randall Billi,
Geertzen Jeroen,
Tyler Lorraine K.
Publication year - 2016
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12234
Subject(s) - optimal distinctiveness theory , feature (linguistics) , lexical decision task , meaning (existential) , representation (politics) , psychology , word (group theory) , natural language processing , computer science , cognitive psychology , task (project management) , cognition , linguistics , social psychology , philosophy , management , neuroscience , politics , political science , law , economics , psychotherapist
Abstract Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature‐based conceptual account assumes that the statistical characteristics of concepts’ features—the number of concepts they occur in ( distinctiveness / sharedness ) and likelihood of co‐occurrence ( correlational strength )—determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech‐to‐meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision that require access to them. Correlational strength facilitated responses for slower participants, suggesting a time‐sensitive co‐occurrence‐driven settling mechanism. The computational simulation showed similar effects, with early effects of shared features and later effects of correlational strength. These results support a general‐to‐specific account of conceptual processing, whereby early activation of shared features is followed by the gradual emergence of a specific target representation.