title: Can a Bayesian analysis account for systematic errors in judgments of 3D shape from texture? A reply to Saunders and Chen.
abstract: Saunders and Chen (2015) have recently proposed a Bayesian model of the perception of 3D shape from texture that they claim is superior to an alternative model based on scaling contrast that was originally proposed by Todd, Thaler, Dijkstra, Koenderink, and Kappers (2007). This commentary will review a variety of empirical findings that are relevant to this issue, and it will also evaluate how well these findings can be explained by different possible models that have been proposed in the literature. The results will demonstrate that the scaling contrast model can account for almost all of the factors that can influence apparent shape from texture with just two free parameters. The Bayesian model of Saunders and Chen has a greater number of free parameters than the scaling contrast model, yet it is not sufficiently developed to make quantitative predictions about any of these factors. Moreover, there are several other processes that would be necessary to actually implement their model that are not mentioned in their theoretical discussion. These include some mechanism for measuring veridical slant from texture, and a mechanism for computing global 3D shape from local estimates of optical slant.
authors.forename: James T
authors.initials: JT
authors.lastname: Todd
mesh_terms: D001499:Bayes Theorem; D003463:Cues; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D021621:Imaging, Three-Dimensional; D007600:Judgment