Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Tal Golan; Johnmark Taylor; Heiko Schütt; Benjamin Peters; Rowan P. Sommers; Katja Seeliger; Adrien Doerig; Paul Linton; Talia Konkle; Marcel Van Gerven; Konrad Kording; Blake Richards; Tim C. Kietzmann; Grace W. Lindsay; Nikolaus Kriegeskorte

doi:10.1017/S0140525X23001553

Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Tal Golan, Johnmark Taylor, Heiko Schütt, Benjamin Peters, Rowan P. Sommers, Katja Seeliger, Adrien Doerig, Paul Linton, Talia Konkle, Marcel Van Gerven, Konrad Kording, Blake Richards, Tim C. Kietzmann, Grace W. Lindsay, Nikolaus Kriegeskorte

Research output: Contribution to journal › Review article › peer-review

Abstract / Description of output

An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

Original language	English
Article number	e392
Journal	Behavioral and Brain Sciences
Volume	46
DOIs	https://doi.org/10.1017/S0140525X23001553
Publication status	Published - 6 Dec 2023

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10.1017/S0140525X23001553

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Golan, T., Taylor, J., Schütt, H., Peters, B., Sommers, R. P., Seeliger, K., Doerig, A., Linton, P., Konkle, T., Van Gerven, M., Kording, K., Richards, B., Kietzmann, T. C., Lindsay, G. W., & Kriegeskorte, N. (2023). Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses. Behavioral and Brain Sciences, 46, Article e392. https://doi.org/10.1017/S0140525X23001553

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abstract = "An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.",

author = "Tal Golan and Johnmark Taylor and Heiko Sch{\"u}tt and Benjamin Peters and Sommers, {Rowan P.} and Katja Seeliger and Adrien Doerig and Paul Linton and Talia Konkle and {Van Gerven}, Marcel and Konrad Kording and Blake Richards and Kietzmann, {Tim C.} and Lindsay, {Grace W.} and Nikolaus Kriegeskorte",

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language = "English",

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journal = "Behavioral and Brain Sciences",

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Golan, T, Taylor, J, Schütt, H, Peters, B, Sommers, RP, Seeliger, K, Doerig, A, Linton, P, Konkle, T, Van Gerven, M, Kording, K, Richards, B, Kietzmann, TC, Lindsay, GW & Kriegeskorte, N 2023, 'Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses', Behavioral and Brain Sciences, vol. 46, e392. https://doi.org/10.1017/S0140525X23001553

TY - JOUR

T1 - Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

AU - Golan, Tal

AU - Taylor, Johnmark

AU - Schütt, Heiko

AU - Peters, Benjamin

AU - Sommers, Rowan P.

AU - Seeliger, Katja

AU - Doerig, Adrien

AU - Linton, Paul

AU - Konkle, Talia

AU - Van Gerven, Marcel

AU - Kording, Konrad

AU - Richards, Blake

AU - Kietzmann, Tim C.

AU - Lindsay, Grace W.

AU - Kriegeskorte, Nikolaus

PY - 2023/12/6

Y1 - 2023/12/6

N2 - An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

AB - An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

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DO - 10.1017/S0140525X23001553

M3 - Review article

C2 - 38054329

AN - SCOPUS:85179022645

SN - 0140-525X

VL - 46

JO - Behavioral and Brain Sciences

JF - Behavioral and Brain Sciences

M1 - e392

ER -

Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

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