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.

Articles

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.

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.