Deep learning models for the perception of human social interactions
Name
1127630161-MIT.pdf
Size
9.63 MB
Format
Adobe PDF
Checksum (MD5)
75ef964219c4c3080bf6e4ff15819084
Author(s)
Eastman, Elizabeth Merritt.
Advisor(s)
Nancy Kanwisher.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Social interaction perception is an important part of humans' visual experience. How- ever, 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. Specically, I implement dierent deep learning computer vision models and test their performance on a social inter- action detection task as well as their match to neural data from the same task. I nd that detecting social interactions most likely requires extensive cortical process- ing and/or recurrent computations. In addition, I nd that experience with action recognition does not improve social interaction detection.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-61).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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