Human visual cortical responses to specular and matte motion flows
- Authors
- Kam, Tae-Eui; Mannion, Damien J.; Lee, Seong-Whan; Doerschner, Katja; Kersten, Daniel J.
- Issue Date
- 20-10월-2015
- Publisher
- FRONTIERS MEDIA SA
- Keywords
- visual perception; surface materials; motion flow; functional magnetic resonance imaging (fMRI); classification
- Citation
- FRONTIERS IN HUMAN NEUROSCIENCE, v.9
- Indexed
- SCIE
SCOPUS
- Journal Title
- FRONTIERS IN HUMAN NEUROSCIENCE
- Volume
- 9
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/92177
- DOI
- 10.3389/fnhum.2015.00579
- ISSN
- 1662-5161
- Abstract
- Determining the compositional properties of surfaces in the environment is an important visual capacity. One such property is specular reflectance, which encompasses the range from matte to shiny surfaces. Visual estimation of specular reflectance can be informed by characteristic motion profiles; a surface with a specular reflectance that is difficult to determine while static can be confidently disambiguated when set in motion. Here, we used fMRI to trace the sensitivity of human visual cortex to such motion cues, both with and without photometric cues to specular reflectance. Participants viewed rotating blob-like objects that were rendered as images (photometric) or dots (kinematic) with either matte consistent or shiny consistent specular reflectance profiles. We were unable to identify any areas in low and mid-level human visual cortex that responded preferentially to surface specular reflectance from motion. However, univariate and multivariate analyses identified several visual areas; Vi, V2, V3, V3A/B, and hMT+, capable of differentiating shiny from matte surface flows. These results indicate that the machinery for extracting kinematic cues is present in human visual cortex, but the areas involved in integrating such information with the photometric cues necessary for surface specular reflectance remain unclear.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.