Login / Signup

EXPRESS: Contextual Dynamics in Lexical Encoding across the Aging Spectrum: A Simulation Study.

Brendan T JohnsVanessa TalerMike Jones
Published in: Quarterly journal of experimental psychology (2006) (2022)
The field of psycholinguistics has recently questioned the primacy of word frequency (WF) in influencing word recognition and production, instead focusing on the importance of a word's contextual diversity (CD). WF is operationalized by counting the number of occurrences of a word in a corpus, while a word's CD is a count of the number of contexts that a word occurs in, with repetitions within a context being ignored. Numerous studies have converged on the conclusion that CD is a better predictor of word recognition latency and accuracy than frequency (see Jones, Johns, & Dye, 2017 for a review). These findings support a cognitive mechanism based on the principle of likely need over the principle of repetition in lexical organization. In the current study, we trained the semantic distinctiveness model of Johns (2021a) on communication patterns in social media platforms consisting of over 55-billion-word tokens and examined the ability of theoretically distinct models to explain word recognition latency and accuracy data from over one million participants from the Mandera et al. (2020) English Crowdsourding Project norms, consisting of approximately 59,000 words across six age bands ranging from ages 10-60. There was a clear quantitative trend across the age bands, where there is a shift from a social environment-based attention mechanism in the "younger" models, to a clear dominance for a discourse-based attention mechanism as models "aged." This pattern suggests that there is a dynamical interaction between the cognitive mechanisms of lexical organization and environmental information that emerges across aging.
Keyphrases
  • social media
  • working memory
  • healthcare
  • health information
  • mental health
  • quality improvement
  • mass spectrometry
  • density functional theory
  • artificial intelligence
  • big data
  • body composition