Technical Program

Paper Detail

Paper: GS-5.2
Session: Contributed Talks V
Location: Ormandy
Session Time: Friday, September 7, 11:00 - 12:00
Presentation Time:Friday, September 7, 11:20 - 11:40
Presentation: Oral
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: From Pixels to Scene Categories: Unique and Early Contributions of Functional and Visual Features
Manuscript:  Click here to view manuscript
Authors: Michelle R. Greene, Bates College, United States; Bruce C. Hansen, Colgate University, United States
Abstract: Human scene categorization is rapid and robust, but we have little understanding of how individual features contribute to categorization, nor the time scale of their contribution. This issue is compounded by the non-independence of the many candidate features. Here we used singular value decomposition to orthogonalize 11 different scene descriptors that included both visual and semantic features. Using high-density EEG and regression analyses, we observed that most explained variability was carried by a late layer of a deep convolutional neural network, as well as a model of a scene’s functions given by the American Time Use Survey. Furthermore, features that explained more variance also tended to explain earlier variance. These results extend previous large-scale behavioral results showing the importance of functional features for scene categorization. Furthermore, these results fail to support models of visual perception that are encapsulated from higher-level cognitive attributes.