Modeling categorization of scenes containing consistent versus inconsistent objects.

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.