Quantifying flexibility in thought: The resiliency of semantic networks differs across the lifespan

Abigail L. Cosgrove, Yoed N. Kenett, Roger E. Beaty, Michele T. Diaz

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

Older adults tend to have a broader vocabulary compared to younger adults – indicating a richer storage of semantic knowledge – but their retrieval abilities decline with age. Recent advances in quantitative methods based on network science have investigated the effect of aging on semantic memory structure. However, it is yet to be determined how this aging effect on semantic memory structure relates to its overall flexibility. Percolation analysis provides a quantitative measure of the flexibility of a semantic network, by examining how a semantic memory network is resistant to “attacks” or breaking apart. In this study, we incorporated percolation analyses to examine how semantic networks of younger and older adults break apart to investigate potential age-related differences in language production. We applied the percolation analysis to 3 independent sets of data (total N = 78 younger, 78 older adults) from which we generated semantic networks based on verbal fluency performance. Across all 3 datasets, the percolation integrals of the younger adults were larger than older adults, indicating that older adults' semantic networks were less flexible and broke down faster than the younger adults'. Our findings provide quantitative evidence for diminished flexibility in older adults' semantic networks, despite the stability of semantic knowledge across the lifespan. This may be one contributing factor to age-related differences in language production.

Original languageEnglish (US)
Article number104631
JournalCognition
Volume211
DOIs
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Language and Linguistics
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Cognitive Neuroscience

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