Technical Program

Paper Detail

Paper: PS-2B.30
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: Visual representations supporting category-specific information about visual objects in the brain
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Simon Faghel-Soubeyrand, Université de Montréal, Canada; Arjen Alink, University of Hamburg, Germany; Eva Bamps, University of Birmingham, Germany; Frédéric Gosselin, Université de Montréal, Canada; Ian Charest, University of Birmingham, Canada
Abstract: Over recent years, multivariate pattern analysis (“decoding”) approaches have become increasingly used to investigate “when” and “where” our brains conduct meaningful processes about their visual environments. Studies using time-resolved decoding of M/EEG patterns have described numerous processes such as object/face familiarity and the emergence of basic-to-abstract category information. Surprisingly, no study has, to our knowledge, revealed “what” (i.e. the actual visual information that) our brain uses while these computations are examined by decoding algorithms. Here, we revealed the time course at which our brain extracts realistic category-specific information about visual objects (i.e. emotion-type & gender information from faces) with time-resolved decoding of high-density EEG patterns, as well as carefully controlled tasks and visual stimulation. Then, we derived temporal generalization matrices and showed that category-specific information is 1) first diffused across brain areas (250 to 350 ms) and 2) encoded under a stable neural pattern that suggests evidence accumulation (350 to 650 ms after face onset). Finally, we bridged time-resolved decoding with psychophysics and revealed the specific visual information (spatial frequency, feature position & orientation information) that support these brain computations. Doing so, we uncovered interconnected dynamics between visual features, and the accumulation and diffusion of category-specific information in the brain.