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Mapping the Structure of Semantic Memory
Author(s) -
Morais Ana Sofia,
Olsson Henrik,
Schooler Lael J.
Publication year - 2012
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12013
Subject(s) - aggregate (composite) , computer science , artificial intelligence , semantic memory , snowball sampling , cluster analysis , sampling (signal processing) , natural language processing , meaning (existential) , psychology , mathematics , statistics , cognition , psychotherapist , materials science , filter (signal processing) , neuroscience , composite material , computer vision
Aggregating snippets from the semantic memories of many individuals may not yield a good map of an individual’s semantic memory. The authors analyze the structure of semantic networks that they sampled from individuals through a new snowball sampling paradigm during approximately 6 weeks of 1‐hr daily sessions. The semantic networks of individuals have a small‐world structure with short distances between words and high clustering. The distribution of links follows a power law truncated by an exponential cutoff, meaning that most words are poorly connected and a minority of words has a high, although bounded, number of connections. Existing aggregate networks mirror the individual link distributions, and so they are not scale‐free, as has been previously assumed; still, there are properties of individual structure that the aggregate networks do not reflect. A simulation of the new sampling process suggests that it can uncover the true structure of an individual’s semantic memory.