  Concepts, both abstract and concrete, elicit a distribution of association
strengths across perceptual color space, which influence aspects of visual
cognition ranging from object recognition to interpretation of information
visualizations. While prior work has hypothesized that color-concept
associations may be learned from the cross-modal statistical structure of
experience, it has been unclear whether natural environments possess such
structure or, if so, whether learning systems are capable of discovering and
exploiting it without strong prior constraints. We addressed these questions by
investigating the ability of GPT-4, a multimodal large language model, to
estimate human-like color-concept associations without any additional training.
Starting with human color-concept association ratings for 71 color set spanning
perceptual color space (\texttt{UW-71}) and concepts that varied in
abstractness, we assessed how well association ratings generated by GPT-4 could
predict human ratings. GPT-4 ratings were correlated with human ratings, with
performance comparable to state-of-the-art methods for automatically estimating
color-concept associations from images. Variability in GPT-4's performance
across concepts could be explained by specificity of the concept's
color-concept association distribution. This study suggests that high-order
covariances between language and perception, as expressed in the natural
environment of the internet, contain sufficient information to support learning
of human-like color-concept associations, and provides an existence proof that
a learning system can encode such associations without initial constraints. The
work further shows that GPT-4 can be used to efficiently estimate distributions
of color associations for a broad range of concepts, potentially serving as a
critical tool for designing effective and intuitive information visualizations.
