A model of lightness perception guided by probabilistic assumptions about lighting and reflectance
Authors: Richard F Murray
Journal: Journal of Vision (2020)
Discipline: CLINICAL MEDICINE
Keywords: Lightness perception; probabilistic graphical model; lighting conditions; reflectance; computational account
Affiliations: Department of Psychology and Centre for Vision Research, York University, Toronto, Ontario, Canada.

Abstract:  Lightness perception is the ability to perceive black, white, and gray surface colors in a wide range of lighting conditions and contexts. This ability is fundamental for any biological or artificial visual system, but it poses a difficult computational problem, and how the human visual system computes lightness is not well understood. Here I show that several key phenomena in lightness perception can be explained by a probabilistic graphical model that makes a few simple assumptions about local patterns of lighting and reflectance, and infers globally optimal interpretations of stimulus images. Like human observers, the model exhibits partial lightness constancy, codetermination, contrast, glow, and articulation effects. It also arrives at human-like interpretations of strong lightness illusions that have challenged previous models. The model's assumptions are reasonable and generic, including, for example, that lighting intensity spans a much wider range than surface reflectance and that shadow boundaries tend to be straighter than reflectance edges. Thus, a probabilistic model based on simple assumptions about lighting and reflectance gives a good computational account of lightness perception over a wide range of conditions. This work also shows how graphical models can be extended to develop more powerful models of constancy that incorporate features such color and depth. 

Retracted: No

PubPeer comments:
 Murray s proposals rest on assumptions he describes as u201ctentative...chosen because either (a) they reflect simple properties of lighting and reflectance or (b) they are suggested by previous literature, and together they explain a range of phenomena in lightness perception.u201d In terms of previous literature, Murray leans heavily on the assumptions of u201canchoring theory:u201d u201cThe present work can be seen as a computational variant of anchoring theory (Gilchrist etu00a0al., 1999; Gilchrist, 2006). As with anchoring theory, a primary goal of MIR is to explain a wide range of qualitative lightness behaviors using a few general principles.u201d Anchoring theory has been effectively challenged on logical and empirical grounds. Responding to Maniatis (2014) critique of anchoring theory, Gilchrist (2014) states that u201cManiatis may have spotted a previously unnoticed (or at least under-appreciated) contradiction in our writings. Subsequently, with a simple manipulation of the classic demo, Maniatis (2015) falsified the u201canchoring theoryu201d as currently configured, showing that its predictions with respect to how this slightly altered version would be perceived did not align with fact. This being the case, it seems reasonable to ask why Murray did not include Maniatis very simple demonstration in tests of his model. If it was fatal to the u201canchoring theoryu201d of Gilchrist et al (1999) (reiterated in Gilchrist (2006)), it might also be fatal to Murray s u201ccomputational account,u201d and the u201cgeneral principlesu201d which it instantiates. ,