Journal
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-HUMAN PERCEPTION AND PERFORMANCE
Volume 35, Issue 6, Pages 1934-1949Publisher
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/a0016902
Keywords
spoken word recognition; network science; clustering coefficient; small-world network; neighborhood density
Categories
Funding
- National Institutes of Health [R01 DC 006472]
- Mental Retardation and Developmental Disabilities Research Center [P30 HD002528]
- Center for Biobehavioral Neurosciences in Communication Disorders [P30 DC005803]
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Clustering coefficient-a measure derived from the new science of networks-refers to the proportion of phonological neighbors of a target word that are also neighbors of each other. Consider the words bat, hat, and can, all of which are neighbors of the word cat; the words bat and hat are also neighbors of each other. In a perceptual identification task, words with a low clustering coefficient (i.e., few neighbors are neighbors of each other) were more accurately identified than words with a high clustering coefficient (i.e., many neighbors are neighbors of each other). In a lexical decision task, words with a low clustering coefficient were responded to more quickly than words with a high clustering coefficient. These findings suggest that the structure of the lexicon (i.e., the similarity relationships among neighbors of the target word measured by clustering coefficient) influences lexical access in spoken word recognition. Simulations of the TRACE and Shortlist models of spoken word recognition failed to account for the present findings. A framework for a new model of spoken word recognition is proposed.
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