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arxiv: 2602.06792 · v1 · submitted 2026-02-06 · 💻 cs.HC

Redundant is Not Redundant: Automating Efficient Categorical Palette Design Unifying Color & Shape Encodings with CatPAW

Chin Tseng , Arran Zeyu Wang , Ghulam Jilani Quadri , Danielle Albers Szafir This is my paper

Pith reviewed 2026-05-16 06:34 UTC · model grok-4.3

classification 💻 cs.HC
keywords redundant encodingcolor shape palettecategorical visualizationscatterplotpalette design toolCatPAWclass correlationcrowdsourced experiment
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The pith

Redundant color and shape encodings improve accuracy when judging class correlations in scatterplots, with largest gains for five to eight categories.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether pairing colors with shapes to encode categories redundantly helps viewers distinguish groups and assess their relationships in scatterplots. Four crowdsourced experiments measure task accuracy across different numbers of categories and find consistent benefits from redundancy, especially in the five-to-eight range, along with strong interactions between particular colors and shapes. These results are turned into CatPAW, an automated tool that generates palettes by selecting empirically effective combinations. A reader would care because visualization designers routinely combine these channels without clear rules, and better choices could reduce misinterpretation of categorical data.

Core claim

Four crowdsourced experiments demonstrate that redundant color-shape encodings enhance accuracy in assessing class-level correlations in scatterplots, with the strongest benefits for five to eight categories and clear interaction effects between specific colors and shapes. These empirical patterns directly inform the construction of CatPAW, a design tool that produces categorical palettes by unifying color and shape encodings according to the identified high-performing configurations.

What carries the argument

CatPAW, the automated palette design tool that selects and combines colors and shapes into redundant encodings based on the experimental performance data.

If this is right

  • Redundancy produces the largest accuracy gains when scatterplots contain five to eight categories.
  • Interaction effects require deliberate pairing of colors with shapes rather than arbitrary combinations.
  • The CatPAW tool lets designers generate effective palettes without relying on untested assumptions about redundancy.
  • Systematic measurement of combined-channel performance advances knowledge of categorical perception in visualizations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The identified pairings could be tested in other chart types such as maps or parallel coordinates where category distinction matters.
  • Future experiments with domain experts and real datasets over extended sessions would clarify whether the short-task benefits persist.
  • Embedding CatPAW defaults into visualization libraries could shift practice toward empirically supported redundant encodings.
  • Extending the approach to additional visual channels like size or texture might reveal broader rules for multi-channel redundancy.

Load-bearing premise

Performance measured in short crowdsourced tasks on synthetic scatterplots will generalize to expert analysts working with real data and longer viewing times.

What would settle it

A controlled study in which professional analysts judge class correlations on authentic datasets using the tool's recommended redundant palettes versus standard non-redundant ones, checking whether accuracy improvements for five-to-eight categories disappear.

Figures

Figures reproduced from arXiv: 2602.06792 by Arran Zeyu Wang, Chin Tseng, Danielle Albers Szafir, Ghulam Jilani Quadri.

Figure 1
Figure 1. Figure 1: We present CatPAW, a web-based tool for generating effective categorical palettes using a data-driven model built from four crowd-sourced experiments. Users can specify palette requirements, such as the number of categories, must-include colors or shapes, and palette type (color-only, shape-only, or redundant). Based on performance estimates drawn from empirical data, CatPAW suggests a set of optimal palet… view at source ↗
Figure 2
Figure 2. Figure 2: The four color palettes used in Experiment 1 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example stimuli from Experiment 1: scatterplots [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Results from Experiment 1. (a) Line chart show [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example stimuli from Experiment 2: three scat [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: The six shape palettes used in Experiment 2. Each [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: (a) Heatmap showing the average accuracy for each [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example stimuli from Experiment 3, showing scat [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 8
Figure 8. Figure 8: The 39 colors used in Experiment 3. These colors [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example stimuli from Experiment 4, showing scat￾terplots with 3, 6, and 9 categories encoded using redundant color–shape combinations, where each category is repre￾sented by a unique pairing of a color and a geometric shape. This experiment was used to construct pairwise accuracy matrices for all 39 colors × 39 shapes to assess how combined encodings influence perceptual discrimination. 6.1 Experiment Des… view at source ↗
Figure 11
Figure 11. Figure 11: Results from Experiment 4. Average accuracy for [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The CatPAW interface. Users can specify their preferred palette type (color-only, shape-only, color–and-shape, or no preference), select required colors and/or shapes, and enter the desired number of categories. CatPAW then generates candidate palettes, which users can iteratively refine by swapping out unwanted elements with the next highest ranked option. 7.3 Cross-Measure Validation using the Categoric… view at source ↗
Figure 13
Figure 13. Figure 13: Cross-measure validation comparing the model￾predicted ranking of redundant categorical palettes with human accuracy from Experiment 4. Our model predicts a ranking (1–50) for redundant palettes within each cate￾gory number, where a higher rank indicates better expected performance. For validation, we randomly sampled 50 re￾dundant palettes for each category number and repeated this process three times. T… view at source ↗
Figure 14
Figure 14. Figure 14: We compared the model-predicted performance of three groups of redundant categorical palettes across cate￾gory numbers (2–10): CatPAW-generated palettes, designer palettes, and user-selected palettes. Designer palettes were derived from widely used visualization systems (D3, Excel, MATLAB, and Tableau). We paired each system’s color palette with its matching default shape palette and randomly com￾bined th… view at source ↗
Figure 15
Figure 15. Figure 15: Example redundant categorical palettes used in the model-based evaluation of palette quality. The figure shows three groups of palettes for category sizes 3, 6, and 9: CatPAW-generated palettes, designer palettes, and user-selected palettes. palettes tended to yield lower accuracy. The interplay between color and shape for redundant encoding highlights the importance of a more holistic orientation to visu… view at source ↗
read the original abstract

Colors and shapes are commonly used to encode categories in multi-class scatterplots. Designers often combine the two channels to create redundant encodings, aiming to enhance class distinctions. However, evidence for the effectiveness of redundancy remains conflicted, and guidelines for constructing effective combinations are limited. This paper presents four crowdsourced experiments evaluating redundant color-shape encodings and identifying high-performing configurations across different category numbers. Results show that redundancy significantly improves accuracy in assessing class-level correlations, with the strongest benefits for 5-8 categories. We also find pronounced interaction effects between colors and shapes, underscoring the need for careful pairing in designing redundant encodings. Drawing on these findings, we introduce a categorical palette design tool that enables designers to construct empirically grounded palettes for effective categorical visualization. Our work advances understanding of categorical perception in data visualization by systematically identifying effective redundant color-shape combinations and embedding these insights into a practical palette design tool.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that redundant color-shape encodings improve accuracy in assessing class-level correlations in multi-class scatterplots (strongest for 5-8 categories), identifies interaction effects between the channels, and introduces the CatPAW tool to automate empirically grounded categorical palette design. These conclusions rest on four crowdsourced experiments using synthetic scatterplots.

Significance. If the results hold, the work would supply concrete empirical guidance for combining color and shape in categorical visualizations and deliver a usable design tool, advancing perceptual understanding in visualization and HCI.

major comments (2)
  1. [Experiments 1-4] The four experiments are presented without participant counts, exclusion criteria, statistical tests, or error bars, so the reported accuracy gains and the claim that redundancy 'significantly improves' performance cannot be verified.
  2. [Discussion and Implications] The practical recommendations for palette design (including CatPAW) rest on the untested assumption that short crowdsourced tasks on synthetic data generalize to expert analysts, real datasets, and longer viewing times; no transfer evidence is supplied.
minor comments (1)
  1. [Results figures] Figure captions and axis labels in the result plots should explicitly state the number of categories and the exact correlation-assessment metric used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We will revise the manuscript to address the reporting of experimental details and to strengthen the discussion of limitations and implications.

read point-by-point responses

  1. Referee: [Experiments 1-4] The four experiments are presented without participant counts, exclusion criteria, statistical tests, or error bars, so the reported accuracy gains and the claim that redundancy 'significantly improves' performance cannot be verified.

    Authors: We agree that these details are necessary for full verification. We will revise the methods and results sections to report the exact participant counts per experiment, the exclusion criteria (including attention checks and performance thresholds), the complete statistical tests performed (including test statistics, degrees of freedom, p-values, and effect sizes), and ensure all figures include clearly labeled error bars. These elements were collected during the studies but were inadvertently omitted from the initial submission due to length constraints. revision: yes

  2. Referee: [Discussion and Implications] The practical recommendations for palette design (including CatPAW) rest on the untested assumption that short crowdsourced tasks on synthetic data generalize to expert analysts, real datasets, and longer viewing times; no transfer evidence is supplied.

    Authors: We acknowledge this limitation. Our experiments were designed as controlled perceptual studies to isolate channel interactions, and we do not provide direct evidence of transfer to expert users, real-world data, or extended sessions. In the revision we will expand the discussion to explicitly note these boundaries, qualify the recommendations accordingly, and suggest targeted follow-up studies. The CatPAW tool remains grounded in the observed perceptual patterns from the controlled tasks, which we believe still offers immediate practical utility while highlighting the need for further validation. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical study with no derivations or self-referential fits

full rationale

The paper reports four crowdsourced experiments measuring accuracy on synthetic scatterplots for redundant color-shape encodings. All central claims (redundancy improves accuracy, strongest for 5-8 categories, interaction effects) are direct statistical outcomes from participant data. No equations, fitted parameters renamed as predictions, self-citation load-bearing premises, or ansatzes appear in the derivation chain. The CatPAW tool is constructed from these experimental results rather than any closed loop. The study is self-contained against its own benchmarks and contains no load-bearing steps that reduce to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of crowdsourced perceptual experiments and standard statistical inference; no free parameters, new physical entities, or ad-hoc axioms are introduced in the abstract.

axioms (1)
  • domain assumption Standard assumptions of crowdsourced perceptual experiments (e.g., participant attention, task comprehension)
    Implicit in any online study claiming general perceptual results.

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