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  1. Home
  3. Large Language Models Estimate Fine-grained Human Color-concept Associations.
  1. Home
  3. Large Language Models Estimate Fine-grained Human Color-concept Associations.

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Related Experiment Video

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Large Language Models Estimate Fine-Grained Human Color-Concept Associations.

Kushin Mukherjee1, Ankit Mohapatra2,3, Timothy T Rogers2,4

  • 1Department of Psychology, Stanford University.

Cognitive Science
|June 22, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models like GPT-4 can learn color-concept associations from data, similar to humans. These machine-generated associations can improve information visualizations for better visual communication.

Keywords:
Color semanticsColor‐concept associationsVision‐language models

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Area of Science:

  • Cognitive Science
  • Computer Science
  • Information Visualization

Background:

  • Humans consistently link abstract and concrete word meanings to colors across color space.
  • This color-concept association impacts visual cognition, object recognition, and interpreting data visualizations.
  • Previous theories suggested cross-modal statistical structures in experience drive these associations, but their presence and learnability were unclear.

Purpose of the Study:

  • To investigate if a multimodal large language model (GPT-4) can estimate human-like color-concept association ratings.
  • To determine if natural environmental data contains sufficient structure for learning these associations without strong prior constraints.

Main Methods:

  • Tested GPT-4's ability to rate associations between 71 colors and various concepts (abstract and concrete).
  • Compared GPT-4's ratings with human ratings across different prompting strategies.
  • Evaluated information visualization palettes generated using GPT-4's ratings in an empirical user study.

Main Results:

  • GPT-4's color-concept ratings showed strong correlations with human ratings.
  • GPT-4 outperformed previous state-of-the-art methods in automatically estimating color-concept associations.
  • Information visualization palettes derived from GPT-4's data were interpretable and, in some cases, more effective than human-rated palettes.

Conclusions:

  • High-order statistical patterns between language and perception in large-scale data are sufficient for learning color-concept associations without initial constraints.
  • Machine-derived color-concept associations can effectively optimize information visualizations for enhanced visual communication.