Tuned representational similarity analysis: Improving the fit between 
computational models of vision and brain data

Kaniuth, Philipp; Hebart, Martin N.

doi:10.1167/jov.20.11.1076

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Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data

Kaniuth, P., & Hebart, M. N. (2020). Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. Journal of Vision, 20: 1076. doi:10.1167/jov.20.11.1076.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-E2F2-5 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-E2F3-4

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Creators:
Kaniuth, Philipp¹, Author
Hebart, Martin N.¹, Author

Affiliations:
1Max Planck Research Group Vision and Computational Cognition, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3158378

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Language(s): eng - English

Dates: Published Online: 2020-10-01

Publication Status: Published online

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Identifiers: DOI: 10.1167/jov.20.11.1076

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Title: Vision Sciences Society Annual Meeting 2020

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Start-/End Date: 2020-06-19 - 2020-06-24

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Title: Journal of Vision

Source Genre: Journal

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Publ. Info: Charlottesville, VA : Scholar One, Inc.

Pages: - Volume / Issue: 20 Sequence Number: 1076 Start / End Page: - Identifier: ISSN: 1534-7362
CoNE: https://pure.mpg.de/cone/journals/resource/111061245811050