Publications of Martin N. Hebart

Schmidt, F., Hebart, M. N., Schmid, A. C., & Fleming, R. W. (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

Badwal, M. W., 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

Contier, O., Baker, C. I., & Hebart, M. N. (2024). Distributed representations of behaviour-derived object dimensions in the human visual system. Nature Human Behaviour, 8, 2179–2193.

Stoinski, L. M., Perkuhn, J., & 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.

Seeliger, K., & Hebart, M. N. (2024). What comparing deep neural networks can teach us about human vision. Nature Machine Intelligence, 6, 122–123.

Zheng, X. Y., Hebart, M. N., Grill, F., Dolan, R. J., Doeller, C. F., Cools, R., & Garvert, M. (2024). Parallel cognitive maps for multiple knowledge structures in the hippocampal formation. Cerebral Cortex, 34. doi:10.1093/cercor/bhad485

Josephs, E. L., Hebart, M. N., & Konkle, T. (2023). Dimensions underlying human understanding of the reachable world. Cognition, 234. doi:10.1016/j.cognition.2023.105368

Kramer, M. A., Hebart, M. N., Baker, C. I., & Bainbridge, W. A. (2023). The features underlying the memorability of objects. Science Advances, 9. doi:10.1126/sciadv.add2981

Hebart, M. N., Contier, O., Teichmann, L., Rockter, A. H., Zheng, C. Y., Kidder, A., … Baker, C. I. (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

Dima, D. C., Hebart, M. N., & Isik, L. (2023). A data-driven investigation of human action representations. Scientific Reports, 13. doi:10.1038/s41598-023-32192-5

Borghesani, V., Armoza, J., Hebart, M. N., Bellec, P., & Brambati, S. M. (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

Singer, J. J. D., Cichy, R. M., & Hebart, M. N. (2023). The spatiotemporal neural dynamics of object recognition for natural images and line drawings. The Journal of Neuroscience, 43, 484–500.

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

Singer, J., Seeliger, K., Kietzmann, T. C., & 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

Grootswagers, T., Zhou, I., Robinson, A. K., Hebart, M. N., & Carlson, T. A. (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

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

Liu, P., Chrysidou, A., Doehler, J., Hebart, M. N., Wolbers, T., & Kuehn, E. (2021). The organizational principles of de-differentiated topographic maps in somatosensory cortex. ELife, 10. doi:10.7554/eLife.60090

Hebart, M. N., Zheng, C. Y., Pereira, F., & Baker, C. I. (2020). Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature Human Behaviour, 4, 1173–1185.

Hebart, M. N., & Schuck, N. W. (2020). Current topics in computational cognitive neuroscience. Neuropsychologia, 147. doi:10.1016/j.neuropsychologia.2020.107621

Bönstrup, M., Iturrate, I., Hebart, M. N., Censor, N., & Cohen, L. G. (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