  Neural network models of language have long been used as a tool for
developing hypotheses about conceptual representation in the mind and brain.
For many years, such use involved extracting vector-space representations of
words and using distances among these to predict or understand human behavior
in various semantic tasks. Contemporary large language models (LLMs), however,
make it possible to interrogate the latent structure of conceptual
representations using experimental methods nearly identical to those commonly
used with human participants. The current work utilizes three common techniques
borrowed from cognitive psychology to estimate and compare the structure of
concepts in humans and a suite of LLMs. In humans, we show that conceptual
structure is robust to differences in culture, language, and method of
estimation. Structures estimated from LLM behavior, while individually fairly
consistent with those estimated from human behavior, vary much more depending
upon the particular task used to generate responses--across tasks, estimates of
conceptual structure from the very same model cohere less with one another than
do human structure estimates. These results highlight an important difference
between contemporary LLMs and human cognition, with implications for
understanding some fundamental limitations of contemporary machine language.
