  This study evaluates the potential of a large language model for aiding in
generation of semantic feature norms - a critical tool for evaluating
conceptual structure in cognitive science. Building from an existing
human-generated dataset, we show that machine-verified norms capture aspects of
conceptual structure beyond what is expressed in human norms alone, and better
explain human judgments of semantic similarity amongst items that are distally
related. The results suggest that LLMs can greatly enhance traditional methods
of semantic feature norm verification, with implications for our understanding
of conceptual representation in humans and machines.
