Measuring Abstract Mind-Sets Through Syntax: Automating the Linguistic Category Model

Kate M. Johnson-Grey, Reihane Boghrati, Cheryl J. Wakslak, Morteza Dehghani

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Abstraction in language has critical implications for memory, judgment, and learning and can provide an important window into a person’s cognitive abstraction level. The linguistic category model (LCM) provides one well-validated, human-coded approach to quantifying linguistic abstraction. In this article, we leverage the LCM to construct the Syntax-LCM, a computer-automated method which quantifies syntax use that indicates abstraction levels. We test the Syntax-LCM’s accuracy for approximating hand-coded LCM scores and validate that it differentiates between text intended for a distal or proximal message recipient (previously linked with shifts in abstraction). We also consider existing automated methods for quantifying linguistic abstraction and find that the Syntax-LCM most consistently approximates LCM scores across contexts. We discuss practical and theoretical implications of these findings.

Original languageEnglish (US)
Pages (from-to)217-225
Number of pages9
JournalSocial Psychological and Personality Science
Volume11
Issue number2
DOIs
StatePublished - Mar 1 2020
Externally publishedYes

Keywords

  • abstraction
  • construal-level theory
  • language
  • LCM
  • syntax
  • text analysis

ASJC Scopus subject areas

  • Social Psychology
  • Clinical Psychology

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