Clarifying status of DNNs as models of human vision

Jeffrey S Bowers; Gaurav Malhotra; Marin Dujmović; Milton L Montero; Christian Tsvetkov; Valerio Biscione; Guillermo Puebla; Federico Adolfi; John E Hummel; Rachel F Heaton; Benjamin D Evans; Jeffrey Mitchell; Ryan Blything

doi:10.1017/S0140525X23002777

Clarifying status of DNNs as models of human vision

Jeffrey S Bowers, Gaurav Malhotra, Marin Dujmović, Milton L Montero, Christian Tsvetkov, Valerio Biscione, Guillermo Puebla, Federico Adolfi, John E Hummel, Rachel F Heaton, Benjamin D Evans, Jeffrey Mitchell, Ryan Blything

Research output: Contribution to journal › Letter, comment/opinion or interview › peer-review

Abstract

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.

Original language	English
Article number	e415
Journal	Behavioral and Brain Sciences
Volume	46
DOIs	https://doi.org/10.1017/S0140525X23002777
Publication status	Published - 6 Dec 2023

Bibliographical note

Keywords

Neural Networks, Computer
Humans
Cognition

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10.1017/S0140525X23002777

Bowers et al. bbs.response.resubmit.arxiv
© 2023 The Authors. CC BY 4.0
Submitted manuscript, 432 KBLicence: CC BY 4.0

Cite this

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title = "Clarifying status of DNNs as models of human vision",

abstract = "On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.",

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AU - Bowers, Jeffrey S

AU - Malhotra, Gaurav

AU - Dujmović, Marin

AU - Montero, Milton L

AU - Tsvetkov, Christian

AU - Biscione, Valerio

AU - Puebla, Guillermo

AU - Adolfi, Federico

AU - Hummel, John E

AU - Heaton, Rachel F

AU - Evans, Benjamin D

AU - Mitchell, Jeffrey

AU - Blything, Ryan

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AB - On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.

KW - Neural Networks, Computer

KW - Humans

KW - Cognition

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M3 - Letter, comment/opinion or interview

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SN - 0140-525X

VL - 46

JO - Behavioral and Brain Sciences

JF - Behavioral and Brain Sciences

M1 - e415

ER -

Clarifying status of DNNs as models of human vision

Abstract

Bibliographical note

Keywords

Access to Document

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Deep Problems with Neural Network Models of Human Vision

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