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

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.

► Previous studies suggest the mental lexicon has a small-world network structure.
► Furthermore, this structure influences certain language-related processes.
► The present study examined how network structure influences LTM and STM.
► The results are accounted for in the complex network framework.