How does the scoring system work?

The 3×3 competency model

ColorAptitude assesses colour competency across nine cells: three colour dimensions (hue, lightness, chroma) crossed with three competency layers (discrimination, attribution, communication). Each assessment falls in one or more cells of this matrix.

Discrimination is perceiving that two stimuli differ from each other. Attribution is recognising which property changed. Communication is correctly naming that difference in professional terminology.

Discrimination score — FM100 error model

The discrimination assessments are based on the error model of Farnsworth (1957). For each placed chip the partial error score is calculated: |position(previous) − position(placed)| + |position(next) − position(placed)| − 2. The sum across all chips gives the Total Error Score (TES).

A TES of 0 is a perfect ordering. The TES is converted to a 0–100 scale where higher is better — so that scores across different assessments are directly comparable.

For norm comparison, TES values are transformed as √TES, following Kinnear & Sahraie (2002). This stabilises age-related variance.

Age-corrected norm comparison

Colour perception measurably declines with age, even without colour vision deficiency. ColorAptitude applies age correction based on the normative data of Kinnear & Sahraie (2002) — 382 screened trichromats, per age year from 5–22 and per decade from 30–79.

A score of 75 at age 40 has a different meaning than the same score at age 25. The corrected score shows how you perform compared to others in your age group.

OKLCH — perceptual colour space and distance metric

All stimuli are generated in the OKLCH colour space (Ottosson, 2020), a perceptually uniform colour space designed so that equal numerical steps correspond to equally perceived colour differences. ColorAptitude uses OKLCH-based distances throughout the assessment for stimulus spacing, threshold scaling and magnitude judgements.

CIEDE2000 (Sharma et al., 2005) is the historical industry standard and remains in use for cross-reference where industry partners report deviations in that metric, but it is not the basis of the ColorAptitude scoring pipeline.

Attribution and communication

For attribution and communication the corrected score is calculated as: (correct − chance) / (1 − chance) × 100. This eliminates the effect of guessing on multiple-choice questions. Chance values per instrument: ⅓ for dimension identification (3 options: hue / lightness / chroma), ¼ for hue naming (4-option protocol), and 1⁄9 for combined dimension + magnitude tasks.

Error classification

In addition to the total error score, ColorAptitude classifies the nature of the errors:

• —Linear errors — small, local shifts (adjacent transpositions).
• —Cluster errors — grouped deviations within the same colour region.
• —Rotation errors — large shifts; cyclic or nearly-opposite transpositions.

Score bands

ColorAptitude reports results at two levels.

Session-total band — the overall result for one assessment session, used on the certificate.

Per-cell zone — each of the nine cells in the 3×3 matrix is classified separately, using a Bayesian posterior (Beta distribution) with a 95% credible interval. This drives the matrix visualisation and development advice.

Band and zone boundaries are provisional and will be adjusted once the normative data collection is completed. The Uncertain zone is evaluated first; cells with a credible interval wider than 30 points are reported as Uncertain regardless of point estimate.

References

• Farnsworth, D. (1957). The Farnsworth-Munsell 100-Hue Test. Munsell Color Company.
• Kinnear, P. R., & Sahraie, A. (2002). New FM100 norms. British Journal of Ophthalmology, 86(12), 1408–1411.
• Kotterink, M. (2026). A digital platform for professional colour competency. Stichting Xconea.
• Ottosson, B. (2020). A perceptual color space for image processing. bottosson.github.io/posts/oklab/
• Sharma, G., Wu, W., & Dalal, E. N. (2005). CIEDE2000. Color Research & Application, 30(1), 21–30.