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… Click to show full 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.
               
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