期刊
JOURNAL OF MEMORY AND LANGUAGE
卷 67, 期 1, 页码 30-44出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jml.2012.02.008
关键词
Network science; STM; LTM; Clustering coefficient; Mental lexicon
资金
- National Institutes of Health through the Schiefelbusch Institute for Life Span Studies (National Institute on Deafness and Other Communication Disorders (NIDCD) [R01 DC 006472]
- Mental Retardation and Developmental Disabilities Research Center (National Institute of Child Health and Human Development) [P30 HD002528]
- Center for Biobehavioral Neurosciences in Communication Disorders [NIDCD P30 DC005803]
- Australian Research Council [DP0663642]
- Australian Research Council [DP0663642] Funding Source: Australian Research Council
Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological word-forms influenced retrieval from the mental lexicon (that portion of long-term memory dedicated to language) during the on-line recognition and production of spoken words. In the present study we examined how network structure influences other retrieval processes in long- and short-term memory. In a false-memory task examining long-term memory participants falsely recognized more words with low- than high-C. In a recognition memory task examining veridical memories in long-term memory participants correctly recognized more words with low- than high-C. However, participants in a serial recall task examining redintegration in short-term memory recalled lists comprised of high-C words more accurately than lists comprised of low-C words. These results demonstrate that network structure influences cognitive processes associated with several forms of memory including lexical, long-term, and short-term. (C) 2012 Elsevier Inc. All rights reserved.
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