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Michael L. Mack, Thomas J. Palmeri; Modeling categorization of scenes containing consistent versus inconsistent objects. Journal of Vision 2010;10(3):11. doi: https://doi.org/10.1167/10.3.11.
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© ARVO (1962-2015); The Authors (2016-present)
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.
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