Human lightness perception is guided by simple assumptions about reflectance and lighting

摘要

Two successful approaches to understanding lightness perception that have developed along largely independent paths are anchoring theory and Bayesian theories. Anchoring theory is a set of rules that predict lightness percepts under a wide range of conditions (Gilchrist, 2006). Some of these rules are difficult to motivate, e.g., larger surfaces tend to look lighter than small surfaces. Bayesian theories rely on probabilistic assumptions about lighting and surfaces, and model percepts as rational inferences from these assumptions combined with sensory data. Here I reconcile these two approaches by showing that many rules of anchoring theory follow from simple assumptions about lighting and reflectance. I describe a Bayesian theory that makes the following assumptions. (1) Reflectances follow a broad, asymmetric normal distribution. (2) Lighting consists of multiplicative and additive components (Adelson, 2000). (3) The proportion of additive light tends to be low. These assumptions predict the main rules of anchoring theory, including: (a) The highest luminance in a scene looks white, and (b) other luminances have lightnesses that are proportional to luminance. (c) A reflectance range less than 30:1 is adjusted towards 30:1. (d) When a low-luminance region becomes larger, its lightness increases, and the lightness of all other regions also increases. (e) The luminance threshold for glow increases with patch size. (f) Lightness constancy is better in scenes containing many distinct luminance patches. Thus anchoring theory can be formulated naturally in a Bayesian framework, and seemingly idiosyncratic properties of lightness perception are rational consequences of simple assumptions about lighting and reflectance.