Grounding deep neural network predictions of human categorization behavior in understandable functional features: the case of face identity

Daube, C., Xu, T., Zhan, J., Webb, A., Ince, R. A.A. , Garrod, O. G.B. and Schyns, P. G. (2021) Grounding deep neural network predictions of human categorization behavior in understandable functional features: the case of face identity. Patterns, 2(10), 100348. (doi: 10.1016/j.patter.2021.100348) (PMCID:PMC8515012)

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Abstract

Deep neural networks (DNNs) can resolve real-world categorization tasks with apparent human-level performance. However, true equivalence of behavioral performance between humans and their DNN models requires that their internal mechanisms process equivalent features of the stimulus. To develop such feature equivalence, our methodology leveraged an interpretable and experimentally controlled generative model of the stimuli (realistic three-dimensional textured faces). Humans rated the similarity of randomly generated faces to four familiar identities. We predicted these similarity ratings from the activations of five DNNs trained with different optimization objectives. Using information theoretic redundancy, reverse correlation, and the testing of generalization gradients, we show that DNN predictions of human behavior improve because their shape and texture features overlap with those that subsume human behavior. Thus, we must equate the functional features that subsume the behavioral performances of the brain and its models before comparing where, when, and how these features are processed.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Garrod, Dr Oliver and Daube, Dr Christoph and Zhan, Dr Jiayu and Xu, Dr Tian and Webb, Dr Andrew and Schyns, Professor Philippe and Ince, Dr Robin
Authors: Daube, C., Xu, T., Zhan, J., Webb, A., Ince, R. A.A., Garrod, O. G.B., and Schyns, P. G.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Patterns
Publisher:Elsevier (Cell Press)
ISSN:2666-3899
ISSN (Online):2666-3899
Published Online:10 September 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Patterns 2(10): 100348
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172413Brain Algorithmics: Reverse Engineering Dynamic Information Processing Networks from MEG time seriesPhilippe SchynsWellcome Trust (WELLCOTR)107802/Z/15/ZNP - Centre for Cognitive Neuroimaging (CCNi)
172046Visual Commonsense for Scene UnderstandingPhilippe SchynsEngineering and Physical Sciences Research Council (EPSRC)EP/N019261/1NP - Centre for Cognitive Neuroimaging (CCNi)