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)

[img] Text
278852.pdf - Published Version
Available under License Creative Commons Attribution.



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
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 (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
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record

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/ZCentre for Cognitive Neuroimaging
172046Visual Commonsense for Scene UnderstandingPhilippe SchynsEngineering and Physical Sciences Research Council (EPSRC)EP/N019261/1Centre for Cognitive Neuroimaging