Degrees of algorithmic equivalence between the brain and its DNN models

Schyns, P. G. , Snoek, L. and Daube, C. (2022) Degrees of algorithmic equivalence between the brain and its DNN models. Trends in Cognitive Sciences, 26(12), pp. 1090-1102. (doi: 10.1016/j.tics.2022.09.003) (PMID:36216674)

Abstract

Deep neural networks (DNNs) have become powerful and increasingly ubiquitous tools to model human cognition, and often produce similar behaviors. For example, with their hierarchical, brain-inspired organization of computations, DNNs apparently categorize real-world images in the same way as humans do. Does this imply that their categorization algorithms are also similar? We have framed the question with three embedded degrees that progressively constrain algorithmic similarity evaluations: equivalence of (i) behavioral/brain responses, which is current practice, (ii) the stimulus features that are processed to produce these outcomes, which is more constraining, and (iii) the algorithms that process these shared features, the ultimate goal. To improve DNNs as models of cognition, we develop for each degree an increasingly constrained benchmark that specifies the epistemological conditions for the considered equivalence.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Daube, Dr Christoph and Schyns, Professor Philippe and Snoek, Mr Lukas
• Authors: Schyns, P. G., Snoek, L., and Daube, C.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Trends in Cognitive Sciences
• Publisher: Elsevier (Cell Press)
• ISSN: 1364-6613
• ISSN (Online): 1879-307X
• Published Online: 07 October 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Trends in Cognitive Sciences 26(12): 1090-1102
• Publisher Policy: Reproduced under a Creative Commons License

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Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172413		Brain Algorithmics: Reverse Engineering Dynamic Information Processing Networks from MEG time series	Philippe Schyns	Wellcome Trust (WELLCOTR)	107802/Z/15/Z	Centre for Cognitive Neuroimaging
172046		Visual Commonsense for Scene Understanding	Philippe Schyns	Engineering and Physical Sciences Research Council (EPSRC)	EP/N019261/1	Centre for Cognitive Neuroimaging