Complex network structure influences processing in long-term and short-term memory

被引:80
|
作者
Vitevitch, Michael S. [1 ]
Chan, Kit Ying
Roodenrys, Steven [2 ]
机构
[1] Univ Kansas, Dept Psychol, Spoken Language Lab, Lawrence, KS 66045 USA
[2] Univ Wollongong, Sch Psychol, Wollongong, NSW 2522, Australia
基金
澳大利亚研究理事会; 美国国家卫生研究院;
关键词
Network science; STM; LTM; Clustering coefficient; Mental lexicon; CREATING FALSE MEMORIES; PHONOTACTIC PROBABILITY; STOCHASTIC RESONANCE; WORD-FREQUENCY; LEXICAL REPRESENTATIONS; NEIGHBORHOOD DENSITY; SERIAL-RECALL; SPOKEN WORDS; REDINTEGRATION; MODEL;
D O I
10.1016/j.jml.2012.02.008
中图分类号
H0 [语言学];
学科分类号
030303 ; 0501 ; 050102 ;
摘要
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.
引用
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页码:30 / 44
页数:15
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