Testing the Coplanar Ratio Hypothesis of Lightness Perception

Depth effect on lightness revisited: The role of articulation, proximity and fields of illumination

The coplanar ratio principle proposes that when the luminance range in an image is larger than the canonical reflectance range of 30:1, the lightness of a target surface depends on the luminance ratio between that target and its adjacent coplanar neighbor (Gilchrist, 1980). This conclusion is based on experiments in which changes in the perceived target depth produced large changes in its perceived lightness without significantly altering the observers' retinal image. Using the same paradigm, we explored how this depth effect on lightness depends on display complexity (articulation), proximity of the target to its highest coplanar luminance and spatial distribution of fields of illumination. Importantly, our experiments allowed us to test differing predictions made by the anchoring theory (Gilchrist et al., 1999), the coplanar ratio principle, as well as other models. We report three main findings, generally consistent with anchoring theory predictions: (1) Articulation can substantially increase the depth effect. (2) Target lightness depends not on the adjacent luminance but on the highest coplanar luminance, irrespective of its position relative to the target. (3) When a plane contains multiple fields of illumination, target lightness depends on the highest luminance in its field of illumination, not on the highest coplanar luminance.

Lightness, brightness and transparency: a quarter century of new ideas, captivating demonstrations and unrelenting controversy

The past quarter century has witnessed considerable advances in our understanding of Lightness (perceived reflectance), Brightness (perceived luminance) and perceived Transparency (LBT). This review poses eight major conceptual questions that have engaged researchers during this period, and considers to what extent they have been answered. The questions concern 1. the relationship between lightness, brightness and perceived non-uniform illumination, 2. the brain site for lightness and brightness perception, 3 the effects of context on lightness and brightness, 4. the relationship between brightness and contrast for simple patch-background stimuli, 5. brightness "filling-in", 6. lightness anchoring, 7. the conditions for perceptual transparency, and 8. the perceptual representation of transparency. The discussion of progress on major conceptual questions inevitably requires an evaluation of which approaches to LBT are likely and which are unlikely to bear fruit in the long term, and which issues remain unresolved. It is concluded that the most promising developments in LBT are (a) models of brightness coding based on multi-scale filtering combined with contrast normalization, (b) the idea that the visual system decomposes the image into "layers" of reflectance, illumination and transparency, (c) that an understanding of image statistics is important to an understanding of lightness errors, (d) Whittle's logW metric for contrast-brightness, (e) the idea that "filling-in" is mediated by low spatial frequencies rather than neural spreading, and (f) that there exist multiple cues for identifying non-uniform illumination and transparency. Unresolved issues include how relative lightness values are anchored to produce absolute lightness values, and the perceptual representation of transparency. Bridging the gap between multi-scale filtering and layer decomposition approaches to LBT is a major task for future research.

Perceptual learning depends on perceptual constancy

Perceptual learning refers to experience-induced improvements in the pick-up of information. Perceptual constancy describes the fact that, despite variable sensory input, perceptual representations typically correspond to stable properties of objects. Here, we show evidence of a strong link between perceptual learning and perceptual constancy: Perceptual learning depends on constancy-based perceptual representations. Perceptual learning may involve changes in early sensory analyzers, but such changes may in general be constrained by categorical distinctions among the high-level perceptual representations to which they contribute. Using established relations of perceptual constancy and sensory inputs, we tested the ability to discover regularities in tasks that dissociated perceptual and sensory invariants. We found that human subjects could learn to classify based on a perceptual invariant that depended on an underlying sensory invariant but could not learn the identical sensory invariant when it did not correlate with a perceptual invariant. These results suggest that constancy-based representations, known to be important for thought and action, also guide learning and plasticity.