| 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 |
| DOI: |
https://doi.org/10.32470/CCN.2018.1144-0 |
| 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. |
