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One thing that may help move the debate forward is to acknowledge what makes people visually distinguish between background and foreground, taking lessons from cartography and apply it more generally to any statistical graphic.

People may initially think that color is a good cue as to whether a specific object is in the foreground or background, but this is not the case. Take for instance this example below, taken from an ESRI blogpost, Make Maps People Want to Look At: Five primary design principles for cartography by Aileen Buckley.

So if I asked you to say which is the figure (e.g. land mass) and which one is the ground (e.g. water body) which one would you pick? A similar phenonenon also happens with the Rubin vase optical illusion.

Some experimental research I remember reading in Alan MacEachren's How Maps Work suggests that in the above pictures people choose the light and dark areas at an equal frequency for the figure (apparently color hue and saturation is used to determine figure from ground). So color can't intrinsically demarcate whether the background competes with the foreground in any statistical graphic, but other cues can help.

People often associate figures as enclosed objects (this is part of the reason the above map is confusing, in that neither mass is enclosed). This suggests in general (regardless of background color), elements in the plot should have clearly delineated boundaries and elements in the plot should be darker than the background. This probably biases the de facto plot background to white, but having a grey background is not damning. Other aspects can be used to delineate between foreground and background (the ESRI blog post mentions a few of these).

One is the hated Excel drop shadow for graphics (example given here in this newsletter by Dan Carr in figure 2). Although that should come with the caveat that people may interpret the numerical attributes at the location of the shadow instead of the intended element.

Another is using different colors/saturation for the outline of an element in the plot versus the interior fill. Examples are given below, with the leftmost circle an example of a not clearly delineated boundary.

These don't appear to be exhaustive either. For line plots it frequently appears that thicker lines come to the foreground, while thinner lines recede to the background.

This is mainly just intended to be food for thought though: your self-study seems to be pretty exhaustive (and I thank you for some of the resources you provided!) I don't think I disagree with any of the resources you provided, but I'm not sure I grok what Hadley is talking about with his motivation for a default grey background. But personal aesthetic preference for grey backgrounds can be accommodated by making sure elements in the plot come to the foreground (that is what really matters). These lessons can be applied to gridlines as well, and if gridlines help and are unobtrusive (i.e. in the background) they certainly are not chartjunk.