Can ChatGPT actually ‘see’ red? New study results are nuanced

ChatGPT works by analyzing vast amounts of text, identifying patterns and synthesizing them to generate responses to users’ prompts. Color metaphors like “feeling blue” and “seeing red” are commonplace throughout the English language, and therefore comprise part of the dataset on which ChatGPT is trained.

But while ChatGPT has “read” billions of words about what it might mean to feel blue or see red, it has never actually seen a blue sky or a red apple in the ways that humans have. This begs the questions: Do embodied experiences—the capacity of the human visual system to perceive color—allow people to understand colorful language beyond the textual ways ChatGPT does? Or is language alone, for both AI and humans, sufficient to understand color metaphors?

New results from a study published in Cognitive Science led by Professor Lisa Aziz-Zadeh and a team of university and industry researchers offer some insights into those questions, and raise even more.

“ChatGPT uses an enormous amount of linguistic data to calculate probabilities and generate very human-like responses,” said Aziz-Zadeh, the publication’s senior author. “But what we are interested in exploring is whether or not that’s still a form of secondhand knowledge, in comparison to human knowledge grounded in firsthand experiences.”

Aziz-Zadeh is the director of the USC Center for the Neuroscience of Embodied Cognition and holds a joint appointment at the USC Dornsife Brain and Creativity Institute. Her lab uses brain imaging techniques to examine how neuroanatomy and neurocognition are involved in higher order skills including language, thought, emotions, empathy and social communication.

The study’s interdisciplinary team included psychologists, neuroscientists, social scientists, computer scientists and astrophysicists from UC San Diego, Stanford, Université de Montréal, the University of the West of England and Google DeepMind, Google’s AI research company based in London.

ChatGPT understands ‘very pink party’ better than ‘burgundy meeting’

The research team conducted large-scale online surveys comparing four participant groups: color-seeing adults, color-blind adults, painters who regularly work with color pigments, and ChatGPT. Each group was tasked with assigning colors to abstract words like “physics.” Groups were also asked to decipher familiar color metaphors (“they were on red alert”) and unfamiliar ones (“it was a very pink party”), and then to explain their reasoning.

Results show that color-seeing and color-blind humans were surprisingly similar in their color associations, suggesting that contrary to the researchers’ hypothesis, visual perception is not necessarily a requirement for metaphorical understanding. However, painters showed a significant boost in correctly interpreting novel color metaphors. This suggests that hands-on experiences using color unlock deeper conceptual representations of it in language.

ChatGPT also generated highly consistent color associations, and when asked to explain its reasoning, often referenced emotional and cultural associations with various colors. For example, to explain the pink party metaphor, ChatGPT replied that “Pink is often associated with happiness, love, and kindness, which suggest that the party was filled with positive emotions and good vibes.”

However, ChatGPT used embodied explanations less frequently than humans did. It also broke down more often when prompted to interpret novel metaphors (“the meeting made him burgundy”) or invert color associations (“the opposite of green”).

As AI continues to evolve, studies like this underscore the limits of language-only models in representing the full range of human understanding. Future research may explore whether integrating sensory input—such as visual or tactile data—could help AI models move closer to approximating human cognition.

“This project shows that there’s still a difference between mimicking semantic patterns, and the spectrum of human capacity for drawing upon embodied, hands-on experiences in our reasoning,” Aziz-Zadeh said.