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

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

doi:10.1017/S0140525X23001553

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学術論文

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

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Seeliger, Katja
Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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引用

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:. doi:10.1017/S0140525X23001553.

引用: https://hdl.handle.net/21.11116/0000-000E-05F3-A

要旨

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.