Publications of Martin N. Hebart

Hansen, H., & Hebart, M. N. (2022). Semantic features of object concepts generated with GPT-3. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 44). Retrieved from http://hdl.handle.net/21.11116/0000-000B-A38A-1

Singer, J., Karapetian, A., Hebart, M. N., & Cichy, R. (2023). Revealing the locus and content of behaviorally relevant information about real-world scenes in human visual cortex. In Journal of Vision (9th ed., Vol. 23). Charlottesville, VA: Scholar One, Inc. doi:10.1167/jov.23.9.4712

Schmidt, F., Hebart, M. N., Schmid, A., & Fleming, R. W. (2021). The mental representation of materials distilled from >1.5 million similarity judgements. In Journal of Vision (Vol. 21). Charlottesville, VA: Scholar One, Inc. doi:10.1167/jov.21.9.1981

Seeliger, K., Roth, J., Schmid, T., & Hebart, M. N. (2021). Synthesizing preferred stimuli for individual voxels in the human visual system. In Journal of Vision (Vol. 21). Charlottesville, VA: Scholar One, Inc. doi:10.1167/jov.21.9.2311

Kaniuth, P., & Hebart, M. N. (2020). Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. In Journal of Vision (Vol. 20). Charlottesville, VA: Scholar One, Inc. doi:10.1167/jov.20.11.1076

Hebart, M. N. Revealing the core dimensions underlying mental representations of objects. Talk_at_event presented at the CRC Workshop on Cardinal Mechanisms of Perception, Rauischholzhausen Castle, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2CEC-B

Hansen, H., & Hebart, M. N. Semantic features of object concepts generated with GPT-3. Talk_at_event presented at the 44th Annual Conference of the Cognitive Science Society (CogSci), Toronto, ON, Canada. Retrieved from http://hdl.handle.net/21.11116/0000-000B-1F0E-5

Hebart, M. N. Core representational dimensions of visually-perceived objects. Talk_at_event presented at the Mind Brain Annual Meeting (SAMBA), Salzburg, Austria. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2CF8-D

Hebart, M. N. The THINGS initiative: A global initiative of researchers for representative sampling of objects in brains, behavior, and computational models. Talk_at_event presented at the Annual Meeting of the Vision Science Society (VSS) , St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2CFC-9

Hebart, M. N., Perkuhn, J., & Kaniuth, P. Efficiently-generated object similarity scores predicted from human feature ratings and deep neural network activations. Talk_at_event presented at the Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2CFA-B

Kaniuth, P., & Hebart, M. N. Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data. Talk_at_event presented at the 10th IMPRS NeuroCom Summer School, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-A7CB-4

Hebart, M. N. Revealing interpretable representations in artificial and biological vision. Talk_at_event presented at the Japanese Meeting for Human Brain Imaging, National Institute for Physiological Sciences, Okazaki, Japan. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2CDE-B

Hebart, M. N. Revealing the similarities and differences between object representations in humans and DNNs. Talk_at_event presented at the Tagung experimentell arbeitender Psychologen (TeaP), Ulm, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2CE0-7

Hebart, M. N. THINGS: A large-scale global initiative to study the cognitive, computational, and neural mechanisms of object recognition in biological and artificial intelligence. Talk_at_event presented at the NeurIPS workshop “Shared Visual Representations in Human & Machine Intelligence,” Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-A7C9-6

Hebart, M. N., Zheng, C., Pereira, F., & Baker, C. Revealing the multidimensional mental representations of natural objects. Talk_at_event presented at the Neuromatch 2.0, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-A7C7-8

Seeliger, K., Leipe, R., Roth, J., & Hebart, M. N. Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. Poster presented at the Neuro-AI-Talks (NEAT), Osnabrück, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000E-761C-F

Mahner, F., Muttenthaler, L., Güçlü, U., & Hebart, M. N. Dimensions that matter: Interpretable object dimensions in humans and deep neural networks. Poster presented at the 6th Annual Conference on Cognitive Computational Neuroscience (CCN), Oxford, United Kingdom. Retrieved from http://hdl.handle.net/21.11116/0000-000E-7612-9

Seeliger, K., Leipe, R., Roth, J., & Hebart, M. N. Investigating high-level visual cortex preferences through neural network training on large neuroimaging data. Poster presented at the Salzburg Mind Brain Meeting (SAMBA), Salzburg, Austria. Retrieved from http://hdl.handle.net/21.11116/0000-000E-7617-4

Stoinski, L. M., Contier, O., Konkle, T., & Hebart, M. N. Revisiting the animacy, size, and curvature organization of human visual cortex. Poster presented at the 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000E-760A-3

Seeliger, K., Leipe, R., Roth, J., & Hebart, M. N. Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. Poster presented at the 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000E-75DC-7