I'm interested in how people experience color. These pages are a work in progress. As I write this, I'm hoping to try and begin to form an answer to some of the questions raised below.
In the space that the human brain builds to represent the surrounding world, the color that the brain assigns to a surface of an object in this space is dependent on many things in addition to the light coming from the corresponding surface in the real world. This is a known phenomenon, and one of the reasons people developed the field of color appearance models. With this in mind, it is interesting to ask: do modern neural networks for image recognition integrate the same information to identify certain colors? If not, there are likely conditions under which they perform poorly as a result.
The above question can be played in reverse: if neural networks do model colors well, then will inspecting the internals of a trained network help reveal how color might be processed in human brains?
Experiments below are exported from Jupyter Lab notebooks from this project's git repository.