Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing

Barry J. Devereux*, Kirsten I. Taylor, Billi Randall, Jeroen Geertzen, Lorraine K. Tyler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
185 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)325-350
Number of pages26
JournalCognitive Science
Issue number2
Publication statusPublished - 01 Mar 2016
Externally publishedYes


  • Attractor networks
  • Concepts
  • Conceptual structure
  • Connectionist modeling
  • Lexical decision
  • Lexical semantics
  • Semantic features
  • Spoken word processing

ASJC Scopus subject areas

  • Language and Linguistics
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence


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