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Prior research has hypothesized that color\u2013concept associations arise from the cross\u2010modal statistical structure of experience, but it remains unclear whether natural environments contain such structure or whether learning systems can discover it without strong prior constraints. To address these questions, we investigated whether GPT\u20104, a multimodal large language model, can estimate color\u2013concept association ratings that approximate those made by people. We tested 71 colors spanning perceptual color space and a variety of concepts varying in abstractness. GPT\u20104 ratings correlated strongly with human ratings across a range of prompting strategies, outperforming prior state\u2010of\u2010the\u2010art methods for automatically estimating color\u2013concept associations from images. In an empirical study assessing people's ability to interpret the meanings of colors in information visualizations, palettes generated from GPT\u20104's rating data were not only interpretable but, in some cases, more effective than those based on human ratings. Taken together, our results suggest that high\u2010order covariance between language and perception, present in web\u2010scale data, provide sufficient information to learn color\u2013concept associations without initial constraints, and that machine\u2010derived associations can support the optimization of information visualizations for visual communication.<\/jats:p>","DOI":"10.1111\/cogs.70219","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T16:27:13Z","timestamp":1782145633000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Large Language Models Estimate Fine\u2010Grained Human Color\u2013Concept Associations"],"prefix":"10.1111","volume":"50","author":[{"given":"Kushin","family":"Mukherjee","sequence":"first","affiliation":[{"name":"Department of Psychology Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankit","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"Wisconsin Institute for Discovery University of Wisconsin\u2010Madison"},{"name":"Department of Computer Science University of Wisconsin\u2010Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy T.","family":"Rogers","sequence":"additional","affiliation":[{"name":"Wisconsin Institute for Discovery University of Wisconsin\u2010Madison"},{"name":"Department of Psychology University of Wisconsin\u2010Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen B.","family":"Schloss","sequence":"additional","affiliation":[{"name":"Wisconsin Institute for Discovery University of Wisconsin\u2010Madison"},{"name":"Department of Psychology University of Wisconsin\u2010Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_2_16_2_1","doi-asserted-by":"crossref","unstructured":"Abdou M. 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