Publications of Martin N. Hebart
All genres
Journal Article (42)
2025
Journal Article
Hebart, M. N., , & (2025). Core dimensions of human material perception. Proceedings of the National Academy of Sciences of the United States of America, 122. doi:10.1073/pnas.2417202122
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Bergmann, J., Roth, J., Doeller, C. F., & Hebart, M. N. (2025). The scope and limits of fine-grained image and category information in the ventral visual pathway. The Journal of Neuroscience, 45. doi:10.1523/JNEUROSCI.0936-24.2024
, 2024
Journal Article
Contier, O., , & Hebart, M. N. (2024). Distributed representations of behaviour-derived object dimensions in the human visual system. Nature Human Behaviour, 8, 2179–2193.
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Hebart, M. N. (2024). THINGSplus: New norms and metadata for the THINGS database of 1854 object concepts and 26,107 natural object images. Behavior Research Methods, 56, 1583–1603.
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Seeliger, K., & Hebart, M. N. (2024). What comparing deep neural networks can teach us about human vision. Nature Machine Intelligence, 6, 122–123.
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Hebart, M. N., , , Doeller, C. F., , & Garvert, M. (2024). Parallel cognitive maps for multiple knowledge structures in the hippocampal formation. Cerebral Cortex, 34. doi:10.1093/cercor/bhad485
, 2023
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Hebart, M. N., & (2023). Dimensions underlying human understanding of the reachable world. Cognition, 234. doi:10.1016/j.cognition.2023.105368
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Hebart, M. N., , & (2023). The features underlying the memorability of objects. Science Advances, 9. doi:10.1126/sciadv.add2981
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Hebart, M. N., Contier, O., , , , , … (2023). THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior. ELife, 12. doi:10.7554/eLife.82580
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Hebart, M. N., & (2023). A data-driven investigation of human action representations. Scientific Reports, 13. doi:10.1038/s41598-023-32192-5
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Hebart, M. N., , & (2023). The Three Terms Task - An open benchmark to compare human and artificial semantic representations. Scientific Data, 10. doi:10.1038/s41597-023-02015-3
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Hebart, M. N. (2023). The spatiotemporal neural dynamics of object recognition for natural images and line drawings. The Journal of Neuroscience, 43, 484–500.
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Kaniuth, P., & Hebart, M. N. (2022). Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. NeuroImage, 257. doi:10.1016/j.neuroimage.2022.119294
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Hebart, M. N. (2022). From photos to sketches-how humans and deep neural networks process objects across different levels of visual abstraction. Journal of Vision, 22. doi:10.1167/jov.22.2.4
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Hebart, M. N., & (2022). Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams. Scientific Data, 9. doi:10.1038/s41597-021-01102-7
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Muttenthaler, L., & Hebart, M. N. (2021). THINGSvision: A Python toolbox for streamlining the extraction of activations from deep neural networks. Frontiers in Neuroinformatics, 15. doi:10.3389/fninf.2021.679838
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Hebart, M. N., , & (2021). The organizational principles of de-differentiated topographic maps in somatosensory cortex. ELife, 10. doi:10.7554/eLife.60090
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Hebart, M. N., , , & (2020). Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature Human Behaviour, 4, 1173–1185.
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Hebart, M. N., & (2020). Current topics in computational cognitive neuroscience. Neuropsychologia, 147. doi:10.1016/j.neuropsychologia.2020.107621
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Hebart, M. N., , & (2020). Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data. Npj Science of Learning, 5. doi:10.1038/s41539-020-0066-9
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