title: Modeling categorization of scenes containing consistent versus inconsistent objects.
abstract: How does object perception influence scene perception? A recent study of ultrarapid scene categorization (O. R. Joubert, G. A. Rousselet, D. Fize, & M. Fabre-Thorpe, 2007) reported facilitated scene categorization when scenes contained consistent objects compared to when scenes contained inconsistent objects. One proposal for this consistent-object advantage is that ultrarapid scene categorization is influenced directly by ultrarapid recognition of particular objects within the scene. We instead asked whether a simpler mechanism that relied only on scene categorization without any explicit object recognition could explain this consistent-object advantage. We combined a computational model of scene recognition based on global scene statistics (A. Oliva & A. Torralba, 2001) with a diffusion model of perceptual decision making (R. Ratcliff, 1978). This model is sufficient to account for the consistent-object advantage. The simulations suggest that this consistent-object advantage need not arise from ultrarapid object recognition influencing ultrarapid scene categorization, but from the inherent influence certain objects have on the global scene statistics diagnostic for scene categorization.
authors.affiliation: Department of Psychology, Vanderbilt University, Nashville, TN, USA. michael.mack@vanderbilt.edu
authors.forename: Michael L
authors.initials: ML
authors.lastname: Mack
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Palmeri
mesh_terms: D000293:Adolescent; D005260:Female; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D008297:Male; D008959:Models, Neurological; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods; D011930:Reaction Time / Q000502:physiology; D055815:Young Adult