Abstract
Background: One of the best-known depth-inversion illusions (DII) that offers strong evidence for the influence of stored knowledge on the visual input [Bar et al. PNAS2006] is the hollow-face illusion [Gregory, "The intelligent eye", 1970; Georgeson, Perception 1979], where a concave face is misperceived as convex. A general explanation is that lifelong familiarity with convex faces overrides data-driven sensory signals. The convexity bias may also play a role in the illusion [Hill & Bruce, Perception 1994]. To isolate the role of the convexity bias we assessed the illusion strength using computer generated 3D convex and concave ovoid objects (results with similar computer generated face stimuli were reported last year [Farkas et al. VSS2015]). Methods: We modeled 14 ovoid "non-face" objects (7 convex, 7 concave), differing in shape, texture and depth structure. Three types of textures were mapped onto the objects, with random black-and-white square textels (texture elements): (1) "Concave texture": Textel size decreased linearly from periphery to center to provide perspective depth cue favoring concavity. (2) "Convex texture": Opposite size variation (increase from periphery to center), favoring convexity. (3) "Neutral texture": Uniform textel size. Each object oscillated around its vertical axis. Observers had to report whether the object was convex or concave. Results: The results indicate that DII strength depends on both structure and texture. The stimuli that produce the most ambiguous percept are the ones in which the textural depth cues compete against depth structure. Using this method we obtained a psychometric function for the DII tendency elicited by a set of virtual "non-face" objects. Conclusions - Discussion: The data show far weaker DII effects with ovoid stimuli as compared to the hollow-face illusion from our previous studies [Farkas et al. VSS2015]. These stimuli will be used to study differences in DII perception between schizophrenia patients and controls.
Meeting abstract presented at VSS 2016