[splittingTimes]

IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that,

(1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.

(2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range.

(3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second will be most effective for low spatial frequency variations.

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[1] the original link got removed from IBMs website. Back in the day it was under

https://www.research.ibm.com/people/l/lloydt/color/color.HTM

A pdf copy is here:

https://github.com/frankMilde/interesting-reads/blob/master/...

[Daub]

> (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad.

This is very good advice. Generally, hue expresses difference in kind whilst lightness and saturation expresses difference in degree. This is beautifully demonstrated in Nault's study of how children read maps.

Nault, W.H.: Children’s Map Reading Abilities. Geographic Society of Chicago, Newsletter, III (1967)

[RugnirViking]

Is there any online fulltext link to that? It's pretty weird you used the exact same reference with the exact same format as your own earlier comment in this 2021 submission https://news.ycombinator.com/item?id=26489887

the study sounds interesting though

[Daub]

> Is there any online fulltext link to that?

I looked for one but could not find. This is unusual for me as I prefer to have the full text of anything I cite.

For me (someone who has an interest in computational aesthetics) the value of such studies is that they confirm what we already know. I explain the key difference between lightness and hue/saturation to my student in this way:

==> Lightness evolved as a matter of necessity. Any light-dwelling creature without it will quickly become food. This accounts for the co-evolution of so many eye-types (fly, mammal, octopus' etc... all structurally distinct).

==> In primates, Hue/lightness vision evolved in response to the rare treat of fruit-sourced carbohydrates.

In other words: lightness is a requisite of survival, hue/saturation is a pleasurable elaboration. It is likely for this reason that in a traditional art school education, you are taught lightness before colour.

> It's pretty weird you used the exact same reference with the exact same format as your own earlier comment in this 2021 submission https://news.ycombinator.com/item?id=26489887

I had completely forgotten that I had referenced this before. Likely the similarity in format is a result of my having copy/pasted from my co-authored book 'Computational Approaches in the Transfer of Aesthetic Values from Paintings to Photographs'. In this book I address similar subject to the ones raise by the OP.