Using hue/saturation to represent multiple dimensions

I remember reading about this a while ago but I forget where, so I'm trying to 1) confirm that I remembered correctly and 2) find a supporting reference (book, research article)

Suppose I have a data visualization like this:

Columns are groups, rows are categories. Groups are separated by hue, and the frequency of each cell is represented by saturation

The primary goal is to indicate that certain category/group combinations occur more frequently than others (via saturation)

We know that there are colour/lightness constancy effects that change the perceived characteristics of an object depending on its neighbours (think back to the dress colour illusion).

Does this extend to using different facets of colour perception (i.e. saturation and hue) in the same visualization? In other words, if you want to see how saturated a particular cell is (to determine its frequency), is your perception of that saturation impacted by the fact that theres a different hue next to it?

I'm wondering whether it might be better to do away with hue altogether, and instead just spatially separate the columns. That way I rely on using only one colour dimension (saturation) to provide information, but this only matters if I remembered all of this correctly

Suggestions for improving the visualization are welcome, but I'm primarily looking to understand whether this interaction between hue and saturation exists