Uncovering the core dimensions of visual object representations

Project Information

COREDIM

Grant agreement ID: 101039712

DOI

10.3030/101039712

EC signature date 7 June 2022

Start date 1 July 2022

End date 30 June 2027

Funded under

• European Research Council (ERC)

Total cost

€ 1 500 000,00

EU contribution

€ 1 500 000,00

Coordinated by

JUSTUS-LIEBIG-UNIVERSITAET GIESSEN
Germany

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Other (6)

The scope and limits of fine-grained image and category information in the ventral visual pathway

Author(s): Markus W. Badwal, Johanna Bergmann, Johannes Roth, Christian F. Doeller, Martin N. Hebart
Published in: NO JOURNAL TITLE, 2024
Publisher: Cold Spring Harbor Laboratory
DOI: 10.1101/2024.08.04.606507

Dimensions underlying the representational alignment of deep neural networks with humans

Author(s): Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü, Martin N. Hebart
Published in: Arxiv, 2024
Publisher: Cold Spring Harbor Library
DOI: 10.48550/arXiv.2406.19087

“Identifying and characterizing scene representations relevant for categorization behavior”

Author(s): Johannes J.D. Singer, Agnessa Karapetian, Martin N. Hebart, Radoslaw M. Cichy
Published in: 2023
Publisher: Cold Spring Harbor Laboratory
DOI: 10.1101/2023.08.17.553708

Core dimensions of human material perception

Author(s): Filipp Schmidt, Martin N Hebart, Alexandra C. Schmid, Roland Fleming
Published in: NO JOURNAL TITLE, 2022
Publisher: Center for Open Science
DOI: 10.31234/osf.io/jz8ks

A high-throughput approach for the efficient prediction of perceived similarity of natural objects

Author(s): Philipp Kaniuth, Florian P. Mahner, Jonas Perkuhn, Martin N. Hebart
Published in: NO JOURNAL TITLE, 2024
Publisher: Cold Spring Harbor Laboratory
DOI: 10.1101/2024.06.28.601184

Dynamic representation of multidimensional object properties in the human brain

Author(s): Lina Teichmann, Martin N. Hebart, Chris I. Baker
Published in: NO JOURNAL TITLE, 2023
Publisher: Cold Spring Harbor Laboratory
DOI: 10.1101/2023.09.08.556679

Peer reviewed articles (14)

What comparing deep neural networks can teach us about human vision

Author(s): Katja Seeliger, Martin N. Hebart
Published in: Nature Machine Intelligence, Issue 6, 2024, ISSN 2522-5839
Publisher: Nature Springer
DOI: 10.1038/s42256-024-00789-8

How modular are modules in visual cortex?

Author(s): Martin N Hebart
Published in: Brain, Issue 148, 2025, ISSN 0006-8950
Publisher: Oxford University Press (OUP)
DOI: 10.1093/BRAIN/AWAF100

Core dimensions of human material perception

Author(s): Filipp Schmidt, Martin N. Hebart, Alexandra C. Schmid, Roland W. Fleming
Published in: Proceedings of the National Academy of Sciences, Issue 122, 2025, ISSN 0027-8424
Publisher: Proceedings of the National Academy of Sciences
DOI: 10.1073/PNAS.2417202122

THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Author(s): Martin N Hebart, Oliver Contier, Lina Teichmann, Adam H Rockter, Charles Y Zheng, Alexis Kidder, Anna Corriveau, Maryam Vaziri-Pashkam, Chris I Baker
Published in: eLife, Issue 12, 2023, ISSN 2050-084X
Publisher: eLife Sciences Publications, Ltd
DOI: 10.7554/eLife.82580

THINGSplus: New Norms and Metadata for the THINGS Database of 1,854 Object Concepts and 26,107 Natural Object Images

Author(s): Laura Mai Stoinski; Jonas Perkuhn; Martin N Hebart
Published in: Behavior Research Methods, 2023, ISSN 1554-3528
Publisher: Springer
DOI: 10.31234/osf.io/exu9f

Dimensions underlying the representational alignment of deep neural networks with humans

Author(s): Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü, Martin N. Hebart
Published in: Nature Machine Intelligence, Issue 7, 2025, ISSN 2522-5839
Publisher: Nature Springer
DOI: 10.1038/S42256-025-01041-7

Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience

Author(s): Johannes Roth; Yunyan Duan; Florian P. Mahner; Philipp Kaniuth; Thomas S. A. Wallis; Martin N. Hebart
Published in: Communications Psychology, 2025, ISSN 2731-9121
Publisher: Nature Springer
DOI: 10.1038/S44271-025-00236-3

The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings

Author(s): Johannes J.D. Singer; Radoslaw M. Cichy; Martin N. Hebart
Published in: The Journal of Neuroscience, 2022, ISSN 1529-2401
Publisher: Society for Neuroscience
DOI: 10.1101/2022.08.12.503484

The features underlying the memorability of objects

Author(s): Max A. Kramer, Martin N. Hebart, Chris I. Baker, Wilma A. Bainbridge
Published in: Science Advances, Issue 9, 2024, ISSN 2375-2548
Publisher: American Association for the Advancement of Science (AAAS)
DOI: 10.1126/sciadv.add2981

Distributed representations of behavior-derived object dimensions in the human visual system

Author(s): Oliver Contier; Chris I. Baker; Martin N. Hebart
Published in: Nature Human Behaviour, 2023, ISSN 2397-3374
Publisher: Nature Springer
DOI: 10.1038/S41562-024-01980-Y

The Scope and Limits of Fine-Grained Image and Category Information in the Ventral Visual Pathway

Author(s): Markus W. Badwal, Johanna Bergmann, Johannes H.R. Roth, Christian F. Doeller, Martin N. Hebart
Published in: The Journal of Neuroscience, Issue 45, 2025, ISSN 0270-6474
Publisher: Society for Neuroscience
DOI: 10.1523/JNEUROSCI.0936-24.2024

Identifying and characterizing scene representations relevant for categorization behavior

Author(s): Johannes J.D. Singer, Agnessa Karapetian, Martin N. Hebart, Radoslaw M. Cichy
Published in: Imaging Neuroscience, Issue 3, 2025, ISSN 2837-6056
Publisher: MIT Press
DOI: 10.1162/IMAG_A_00449

Distributed representations of behaviour-derived object dimensions in the human visual system

Author(s): Oliver Contier, Chris I. Baker, Martin N. Hebart
Published in: Nature Human Behaviour, 2024, ISSN 2397-3374
Publisher: Springer Science and Business Media LLC
DOI: 10.1038/s41562-024-01980-y

A high-throughput approach for the efficient prediction of perceived similarity of natural objects

Author(s): Philipp Kaniuth; Florian P. Mahner; Jonas Perkuhn; Martin N. Hebart
Published in: eLife, 2025, ISSN 2050-084X
Publisher: eLife Sciences Publications
DOI: 10.1101/2024.06.28.601184