Martin Hebart

Short info

My team and I aim at understanding how humans make sense of the visual world: How do we recognize the objects around us, how does the structure and function of our visual brain support this ability, and what is the role of semantic knowledge in visual processing? Fundamental to our research is the idea that we can identify and study meaningful latent dimensions that underlie our ability to perceive, categorize, and structure our visual experience.

Our research is based on a multidisciplinary approach at the intersection of psychology, neuroscience, and computer science. Our methods include traditional carefully controlled experiments, large-scale data-driven approaches based on massive behavioral and neuroimaging datasets that we collect and analyze (https://things-initiative.org), and computational modeling based on recent developments in artificial intelligence including deep neural networks and large language models.

Open Science

Amme, C., Sulewski, P., Spaak, E., Hebart, M. N., König, P., & Kietzmann, T. C. (2024).
Saccade onset, not fixation onset, best explains early responses across the human visual cortex during naturalistic vision.
bioRxiv preprint<:/em> 2024-10.

Kämmer, L., Kroell, L. M., Knapen, T., Rolfs, M., & Hebart, M. N. (2025).
Feedback of peripheral saccade targets to early foveal cortex.
eLife, 14.

Kaniuth, P., Mahner, F. P., Perkuhn, J., & Hebart, M. N. (2024).
A high-throughput approach for the efficient prediction of perceived similarity of natural objects.
bioRxiv preprint: 2024-06.

Linde-Domingo, J., Ortiz-Tudela, J., Völler, J., Hebart, M. N., & González-García, C. (2025).
Determinants of visual ambiguity resolution.
bioRxiv preprint: 2025-05.

Teichmann, L., Hebart, M. N., & Baker, C. I. (2025).
Dynamic representation of multidimensional object properties in the human brain.
bioRxiv preprint: 2023-09.

van Dyck, L. E., Hebart, M. N., & Dobs, K. (2025).
Multidimensional feature tuning in category-selective areas of human visual cortex.
bioRxiv, 2025-06.

Articles

Bockes, A., Hebart, M. N., & Lingnau, A. (2025).
Revealing Key Dimensions Underlying the Recognition of Dynamic Human Actions.
Communications Psychology, 3(1), 149.

Hebart, M. N. (2025).
How modular are modules in visual cortex?.
Brain, 148(4), 1050-1051.

Hebart, M. N., Contier, O., Teichmann, L., Rockter, A. H., Zheng, C. Y., Kidder, A., Corriveau, A., Vaziri-Pashkam, M., & Baker, C. I. (2023).
THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior.
Elife, 12, e82580.

Kramer, M., Hebart, M. N., Baker, C. I., & Bainbridge, W. A. (2023).
The features underlying the memorability of objects.
Science Advances 9, eadd2981.

Mahner, F. P., Muttenthaler, L., Güçlü, U., & Hebart, M. N. (2025).
Dimensions underlying the representational alignment of deep neural networks with humans.
Nature Machine Intelligence, 7(6), 848-859.

Roth, J., Duan, Y., Mahner, F. P., Kaniuth, P., Wallis, T. S., & Hebart, M. N. (2025).
Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience.
Communications Psychology, 3(1), 62.

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, 122(10), e2417202122.

Stoinski, L. M., Perkuhn, J., & Hebart, M. N. (2023).
THINGSplus: New norms and metadata for the THINGS database of 1,854 object concepts and 26,107 natural object images.
Behavior Research Methods, 1-21.

Sun, H. C., Schmidt, F., Schmid, A. C., Hebart, M. N., & Fleming, R. W. (2024).
Cortical representations of core visual material dimensions.
Journal of Vision, 24(10), 285-285.

van Dyck, L., Hebart, M. N., & Dobs, K. (2024).
Core neural dimensions of functionally selective areas in the human visual cortex.
European Conference on Visual Perception (ECVP).