| Paper: | PS-1A.29 |
| Session: | Poster Session 1A |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Rapid detection of social interactions in the human brain |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1092-0 |
| Authors: |
Leyla Isik, Anna Mynick, Dimitrios Pantazis, Nancy Kanwisher, MIT, United States |
| Abstract: |
Social interaction perception is a crucial part of the human visual experience that develops early in infancy and is shared with other primates. However, it remains largely unknown how humans compute information about others’ social interactions from visual input. Is social interaction detection a rapid perceptual process, or a slower post-perceptual inference? To answer this question, we used magnetoencephalography (MEG) decoding and computational modeling to ask how fast the human brain detects third-party social interactions. In particular, subjects in the MEG viewed snapshots of visually matched real-world scenes containing a pair of people who were either engaged in a social interaction or acting independently. We could read out the presence versus absence of a social interaction from subjects’ MEG data extremely quickly, as early as 150 ms after stimulus onset. We next showed that late, but not early, layers of a purely feedforward convolutional neural network (CNN) model could detect social interactions in the same images and contained representations that matched those in the MEG data. Taken together, these results suggest that the detection of social interactions is a rapid feedforward perceptual process, rather than a slow post-perceptual inference. |
