Statistical or Embodied? Comparing Colorseeing, Colorblind, Painters, and Large Language Models in their Processing of Color Metaphors

Can metaphorical reasoning involving embodied experience—such as color perception—be learned from the statistics of language alone? Recent work finds that colorblind individuals robustly understand and reason abstractly about color, implying that color associations in everyday language might contribute to metaphorical understanding of color. However, it is unclear how much colorblind individuals’ understanding of color is driven by language versus their limited (but no less embodied) visual experience. A more direct test of whether language supports the acquisition of humans’ understanding of color is whether large language models (LLMs)—those trained purely on text with no visual experience—can nevertheless learn to generate consistent and coherent metaphorical responses about color. Here, we conduct pre-registered surveys that compare colorseeing adults, colorblind adults, and LLMs in how they (i) associate colors to words that lack established color associations and (ii) interpret conventional and novel color metaphors. Colorblind and colorseeing adults exhibited highly similar and replicable color associations with novel words and abstract concepts. Yet, while GPT (a popular LLM) also generated replicable color associations with impressive consistency, its associations departed considerably from colorseeing and colorblind participants. Moreover, GPT frequently failed to generate coherent responses about its own metaphorical color associations when asked to invert its color associations or explain novel color metaphors in context. Consistent with this view, painters who regularly work with color pigments were more likely than all other groups to understand novel color metaphors using embodied reasoning. Thus, embodied experience may play an important role in metaphorical reasoning about color and the generation of conceptual connections between embodied associations.