  Semantic dimensions of sound have been playing a central role in
understanding the nature of auditory sensory experience as well as the broader
relation between perception, language, and meaning. Accordingly, and given the
recent proliferation of large language models (LLMs), here we asked whether
such models exhibit an organisation of perceptual semantics similar to those
observed in humans. Specifically, we prompted ChatGPT, a chatbot based on a
state-of-the-art LLM, to rate musical instrument sounds on a set of 20 semantic
scales. We elicited multiple responses in separate chats, analogous to having
multiple human raters. ChatGPT generated semantic profiles that only partially
correlated with human ratings, yet showed robust agreement along well-known
psychophysical dimensions of musical sounds such as brightness (bright-dark)
and pitch height (deep-high). Exploratory factor analysis suggested the same
dimensionality but different spatial configuration of a latent factor space
between the chatbot and human ratings. Unexpectedly, the chatbot showed degrees
of internal variability that were comparable in magnitude to that of human
ratings. Our work highlights the potential of LLMs to capture salient
dimensions of human sensory experience.
