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

Abstract

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.

Patterns in the English language: phonological networks, percolation and assembly models

“…Furthermore, the giant component exhibits the small-word property combined with high cliquishness, a rather high level of assortative mixing by degree, and a degree distribution that has been described as a power-law with cut-off [27,28,29]. These results have been confirmed for phonological networks constructed for various other languages, such as Spanish, Mandarin, Hawaiian, and Basque [29,30].…”

Remotely Close Associations: Openness to Experience and Semantic Memory Structure

“…Semantic memory is an important function in human cognition that affects language, how we categorize information about the world, and our ability to recognize situations. Using network models, we can glean valuable inferences about the development of language, second languages, differences in cognition, and psychological disorder (Borodkin, Kenett, Faust, & Marshal, 2016;De Deyne et al, 2016;Kenett et al, 2016b;Steyvers & Tenenbaum, 2005;Vitevitch, Chan, & Roodenrys, 2012). Representing semantic information in networks allows us to ask many questions: What is the structure of semantic memory?…”

Spatially constrained adaptive rewiring in cortical networks creates spatially modular small world architectures

“…This resulted in networks that have, in addition to small-world, scale-free characteristics (Barabási et al 1999). The psychological relevance of this property is clearly in evidence in the mental lexicon (Ferrer i Cancho and Solé 2001;Vitevitch et al 2012).…”

“…Also, the mental lexicon has small-world structure, according to the graph of co-occurrences of words in sentence contexts (Ferrer i Cancho and Solé 2001). The properties of this structure influence the speed of retrieval from the mental lexicon for recognition (Chan and Vitevitch 2009) and production of spoken word (Chan and Vitevitch 2010), as well as long and short term memory retrieval (Vitevitch et al 2012).…”