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

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.