| Paper: | PS-2B.29 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Local contour symmetry facilitates the neural representation of scene categories in the PPA. | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1099-0 | ||
| Authors: | John Wilder, University of Toronto, Canada; Morteza Rezanejad, Kaleem Siddiqi, McGill University, Canada; Allan Jepson, Sven Dickinson, Samsung Research, Canada; Dirk Walther, University of Toronto, Canada | ||
| Abstract: | Human observers can rapidly classify real-world scenes into their natural categories (e.g. beaches, mountains…). It is unclear what neural mechanisms underlie this rapid processing of scenes. In a previous behavioral study, we demonstrated that local ribbon symmetry facilitates scene classification. Here we manipulate the ribbon symmetry content of line drawings of real-world scenes and then decode scene categories from patterns of voxel activities of the observers obtained via fMRI. We can decode scene categories from the parahippocampal place area (PPA) more easily from symmetric scenes than asymmetric scenes. In earlier visual areas the decoding accuracy for symmetric and asymmetric scenes was not significantly different. This suggests that the benefit for symmetric scenes in both behavior and fMRI is not solely driven by a lower-level preference for symmetry. Instead, ribbon symmetry may be uniquely informative for scene categorization. | ||