  Cross-domain alignment refers to the task of mapping a concept from one
domain to another. For example, ``If a \textit{doctor} were a \textit{color},
what color would it be?''. This seemingly peculiar task is designed to
investigate how people represent concrete and abstract concepts through their
mappings between categories and their reasoning processes over those mappings.
In this paper, we adapt this task from cognitive science to evaluate the
conceptualization and reasoning abilities of large language models (LLMs)
through a behavioral study. We examine several LLMs by prompting them with a
cross-domain mapping task and analyzing their responses at both the population
and individual levels. Additionally, we assess the models' ability to reason
about their predictions by analyzing and categorizing their explanations for
these mappings. The results reveal several similarities between humans' and
models' mappings and explanations, suggesting that models represent concepts
similarly to humans. This similarity is evident not only in the model
representation but also in their behavior. Furthermore, the models mostly
provide valid explanations and deploy reasoning paths that are similar to those
of humans.
