Conference on Cognitive Computational Neuroscience

San Francisco • August 25 - 28, 2022

P-1.37

Spontaneous Learning of Face Identity in Expression-Trained Deep Nets

Emily Schwartz, Stefano Anzellotti, Boston College, United States; Kathryn O'Nell, Dartmouth College, United States; Rebecca Saxe, Massachusetts Institute of Technology, United States
Session:
Posters 1 Poster
Track:
Cognitive science
Location:
Pacific Ballroom H-O
Presentation Time:
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -8)
Abstract:
Recent neural evidence challenges the traditional view that face identity and facial expressions are processed by segregated neural pathways, showing that information about identity and expression are encoded within common brain regions. This article tests the hypothesis that integrated representations of identity and expression arise naturally within neural networks. Deep networks trained to recognize expression and deep networks trained to recognize identity spontaneously develop representations of identity and expression, respectively. These findings serve as a “proof-of-concept” that it is not necessary to discard task-irrelevant information for identity and expression recognition.
Manuscript:
View Manuscript
License:
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI:
10.32470/CCN.2022.1116-0
Publication:
2022 Conference on Cognitive Computational Neuroscience
Presentation
Discussion
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No resources available.
Session P-1
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