Year: 2019
Type: dissertation
Abstract: Social interaction perception is an important part of humans' visual experience.However, little is known about the way the human brain processes visual input in order to understand social interactions.In comparison, other vision problems, such as object recognition tasks, have been studied extensively and seen success by comparing state of the art computer vision models to neuroimaging data.In this thesis, I employ a similar method in order to study social interaction perception with deep learning models and magnetoencephalography (MEG) data.Specifically, I implement different deep learning computer vision models and test their performance on a social interaction detection task as well as their match to neural data from the same task.I find that detecting social interactions most likely requires extensive cortical processing and/or recurrent computations.In addition, I find that experience with action recognition does not improve social interaction detection.
Author: Elizabeth Merritt. Eastman
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2999828089