I’m pretty sure I made a comment about this or a similar post linked here a few years ago, including some graphics cooked up in Photoshop explaining the problems with the examples, but I can’t find it at the moment, and can’t be bothered to make new demonstrations.<p>Suffice it to say: unfortunately the lightness (a.k.a. value) of the color stripes varies tremendously from one example to the other in this post, which makes this a basically worthless comparison, as in general lightness contrast dominates other visual effects. If you swapped the lightness (e.g. in CIELAB or CIECAM02 color space) from the left and right examples while leaving the hue&#x2F;chroma the same, you’d get the opposite results inre what looks “good”, which would reveal this “Müller formula” to be bogus (or at any rate radically oversimplified).<p>More generally, I’ve never seen a convincing exposition of similar color preference effects backed by data collected in a scientific paper with reasonable study design. It’s always handwavey 19th century nonsense, sometimes with a handful of preferences expressed by a tiny sample of homogenous survey respondents tacked on the end. Bad examples are endemic to the pseudosciency-color-theory genre, because most people work with terrible color models which are only marginally relevant to human perception.<p>The key to making color schemes for graphic illustrations or data visualizations look good is to use enough contrast (especially lightness contrast) between intentionally separate shapes so that distinct elements don’t accidentally blend together. (Note that sometimes you want certain elements to have lower contrast, but that should be an intentional choice, made for some obvious reason.)