Modeling categorization of scenes containing consistent versus inconsistent objects
Year: 2010
Type: article
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.
Source: Journal of Vision
DOI: 10.1167/10.3.11
Author: Michael L. Mack
Institution: Vanderbilt University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,850
OpenAlex ID: W2109586375