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What Color Scheme is More Effective in Assisting Readers to Locate Information in a Color-Coded Article?

Ho Yin Ng, Zeyu He, Ting-Hao 'Kenneth' Huang

TL;DR

This paper investigates how color choices affect information-seeking in color-coded documents annotated by LLMs. Ten color schemes derived from four base hues across warm and cool temperatures were evaluated with a standardized highlight-to-text contrast of approximately $5.55:1$, using GPT-4 to label arXiv cs.HCI abstracts according to CODA-19 categories. Results show that non-analogous color schemes improve accuracy and speed over analogous schemes, with dichromatic schemes outperforming monochromatic ones, and yellow-inclusive palettes being both faster and more preferred than others, while red-inclusive palettes are slower and less favored. The findings provide actionable guidance for selecting color palettes in text annotation and underscore the need for further color-focused studies as LLM-based document coding scales up.

Abstract

Color coding, a technique assigning specific colors to cluster information types, has proven advantages in aiding human cognitive activities, especially reading and comprehension. The rise of Large Language Models (LLMs) has streamlined document coding, enabling simple automatic text labeling with various schemes. This has the potential to make color-coding more accessible and benefit more users. However, the impact of color choice on information seeking is understudied. We conducted a user study assessing various color schemes' effectiveness in LLM-coded text documents, standardizing contrast ratios to approximately 5.55:1 across schemes. Participants performed timed information-seeking tasks in color-coded scholarly abstracts. Results showed non-analogous and yellow-inclusive color schemes improved performance, with the latter also being more preferred by participants. These findings can inform better color scheme choices for text annotation. As LLMs advance document coding, we advocate for more research focusing on the "color" aspect of color-coding techniques.

What Color Scheme is More Effective in Assisting Readers to Locate Information in a Color-Coded Article?

TL;DR

This paper investigates how color choices affect information-seeking in color-coded documents annotated by LLMs. Ten color schemes derived from four base hues across warm and cool temperatures were evaluated with a standardized highlight-to-text contrast of approximately , using GPT-4 to label arXiv cs.HCI abstracts according to CODA-19 categories. Results show that non-analogous color schemes improve accuracy and speed over analogous schemes, with dichromatic schemes outperforming monochromatic ones, and yellow-inclusive palettes being both faster and more preferred than others, while red-inclusive palettes are slower and less favored. The findings provide actionable guidance for selecting color palettes in text annotation and underscore the need for further color-focused studies as LLM-based document coding scales up.

Abstract

Color coding, a technique assigning specific colors to cluster information types, has proven advantages in aiding human cognitive activities, especially reading and comprehension. The rise of Large Language Models (LLMs) has streamlined document coding, enabling simple automatic text labeling with various schemes. This has the potential to make color-coding more accessible and benefit more users. However, the impact of color choice on information seeking is understudied. We conducted a user study assessing various color schemes' effectiveness in LLM-coded text documents, standardizing contrast ratios to approximately 5.55:1 across schemes. Participants performed timed information-seeking tasks in color-coded scholarly abstracts. Results showed non-analogous and yellow-inclusive color schemes improved performance, with the latter also being more preferred by participants. These findings can inform better color scheme choices for text annotation. As LLMs advance document coding, we advocate for more research focusing on the "color" aspect of color-coding techniques.
Paper Structure (23 sections, 2 figures, 2 tables)

This paper contains 23 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Color Coding: Enhancing Learning, Reading, and Information Handling
  4. Color in Visualization
  5. User Study
  6. Task Design and Interface
  7. Experiment Material Preparation
  8. Color Schemes Selection
  9. Data Processing and Label Scheme
  10. Data Selection.
  11. Study Procedure and Setups
  12. Participants.
  13. Study Setups.
  14. Study Procedure.
  15. Findings
  16. ...and 8 more sections

Figures (2)

  • Figure 1: The 10 color schemes used in our study, generated by combinations of 4 base colors—warm (Red, Yellow) and cool (Green, Blue). Monochromatic schemes consist of variations of a single base color; Dichromatic schemes are genereated by combining two different base colors; Analogous schemes feature base colors with the same or similar hues; Non-analogous schemes include contrasting base colors.
  • Figure 2: Response Time and Accuracy Plot for 10 Color Scheme. Each color scheme was classified into 'Warm,' 'Cool,' and 'Mixed' categories and was denoted by distinct symbols.