Publications of Martin N. Hebart

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

Contier, O., Fujimori, S., Seeliger, K., Ratan Murty, N. A., & Hebart, M. N. Revealing interpretable object dimensions from a high-throughput model of the fusiform face area. 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-7604-9

St-Laurent, M., Pinsard, B., Contier, O., Seeliger, K., Borghesani, V., Boyle, J., … Hebart, M. N. Cneuromod-things: A large-scale fMRI dataset for task-and data-driven assessment of object representation and visual memory recognition in the human brain. 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-75DE-5

Roth, J., Miyawaki, Y., & Hebart, M. N. Assessing the feasibility of high stimulus presentation rates for contrasting conditions in functional MRI studies. 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-7600-D

Mahner, F., Seeliger, K., Umut, G., & Hebart, M. N. Learning cortical magnification with brain-optimized convolutional neural networks. Poster presented at the Conference on Cognitive Computational Neuroscience, San Francisco, CA, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000B-1E5A-0

Hebart, M. N., Contier, O., Baker, C. I., & Teichmann, L. The THINGS initiative: A global large-scale effort for the representative study of objects in brains, behavior, and computational models. Poster presented at the 47. Jahrestagung Psychologie und Gehirn 2022, Freiburg, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000B-1B3C-5

Kaniuth, P., & Hebart, M. N. Object similarities can be efficiently generated using human ratings and neural network predictions. Poster presented at the 11th IMPRS NeuroCom Summer School, Leipzig, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000A-E2EC-D

Kaniuth, P., Perkuhn, J., & Hebart, M. N. Object similarities can be efficiently generated using human ratings and neural network predictions. Poster presented at the 28th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Glasgow, United Kingdom. Retrieved from http://hdl.handle.net/21.11116/0000-000A-E2F0-7

Hansen, H., & Hebart, M. N. Automatic generation of semantic feature norms of objects using GPT-3. Poster 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-A7F3-6

Stoinski, L., Perkuhn, J., & Hebart, M. N. THINGS+: New norms and metadata for the THINGS database of 1,854 object concepts and 26,107 natural object images. Poster presented at the Vision Science Society Congress, St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000B-1EF1-4

Contier, O., Dickter, A. H., Teichmann , L., & Hebart, M. N. THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images. Poster presented at the Vision Sciences Society Annual Meeting (V-VSS), Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-3178-7

Contier, O., & Hebart, M. N. Distributed representation of behaviorally-relevant object dimensions in the human brain. Poster presented at the Vision Sciences Society Annual Meeting (VSS), St. Pete Beach, FL, USA. Retrieved from https://www.researchgate.net/publication/335962396_Revealing_the_behaviorally-relevant_dimensions_underlying_mental_representations_of_objects

Contier, O., Hebart, M. N., Dickter, A. H., Teichmann, L., Kidder, A., Corriveau, A., … Baker, C. I. THINGS-fMRI/MEG: A densely sampled multimodal neuroimaging dataset of brain responses to a broad range of natural object images. Poster presented at the Society for Neuroscience 50th Annual Meeting, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-A7CD-2

Kaniuth, P., & Hebart, M. N. Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data. Poster presented at the 10th IMPRS NeuroCom Summer School, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000A-E2EA-F

Contier, O., Hebart, M. N., Dickter, A. H., Teichmann, L., Kidder, A., Corriveau, A., … Baker, C. I. THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images. Poster presented at the Vision Sciences Society Annual Meeting (V-VSS), Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-A7E4-7