Publications of Martin N. Hebart

Hebart, M. N.; Zheng, C.; Pereira, F.; Baker, C.: Revealing the multidimensional mental representations of natural objects. Neuromatch 2.0, Virtual (2020)

Seeliger, K.; Leipe, R.; Roth, J.; Hebart, M. N.: Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. Neuro-AI-Talks (NEAT), Osnabrück, Germany (2023)

Mahner, F.; Muttenthaler, L.; Güçlü, U.; Hebart, M. N.: Dimensions that matter: Interpretable object dimensions in humans and deep neural networks. 6th Annual Conference on Cognitive Computational Neuroscience (CCN), Oxford, United Kingdom (2023)

Seeliger, K.; Leipe, R.; Roth, J.; Hebart, M. N.: Investigating high-level visual cortex preferences through neural network training on large neuroimaging data. Salzburg Mind Brain Meeting (SAMBA), Salzburg, Austria (2023)

Stoinski, L. M.; Contier, O.; Konkle, T.; Hebart, M. N.: Revisiting the animacy, size, and curvature organization of human visual cortex. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

Seeliger, K.; Leipe, R.; Roth, J.; Hebart, M. N.: Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

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. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

St-Laurent, M.; Pinsard, B.; Contier, O.; Seeliger, K.; Borghesani, V.; Boyle, J.; Bellec, P.; 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. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

Roth, J.; Miyawaki, Y.; Hebart, M. N.: Assessing the feasibility of high stimulus presentation rates for contrasting conditions in functional MRI studies. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

Mahner, F.; Seeliger, K.; Umut, G.; Hebart, M. N.: Learning cortical magnification with brain-optimized convolutional neural networks. Conference on Cognitive Computational Neuroscience, San Francisco, CA, USA (2022)

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. 47. Jahrestagung Psychologie und Gehirn 2022, Freiburg, Germany (2022)

Kaniuth, P.; Hebart, M. N.: Object similarities can be efficiently generated using human ratings and neural network predictions. 11th IMPRS NeuroCom Summer School, Leipzig, Germany (2022)

Kaniuth, P.; Perkuhn, J.; Hebart, M. N.: Object similarities can be efficiently generated using human ratings and neural network predictions. 28th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Glasgow, United Kingdom (2022)

Hansen, H.; Hebart, M. N.: Automatic generation of semantic feature norms of objects using GPT-3. Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2022)

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. Vision Science Society Congress, St. Pete Beach, FL, USA (2022)

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. Vision Sciences Society Annual Meeting (V-VSS), Virtual (2022)

Contier, O.; Hebart, M. N.: Distributed representation of behaviorally-relevant object dimensions in the human brain. Vision Sciences Society Annual Meeting (VSS), St. Pete Beach, FL, USA (2022)

Contier, O.; Hebart, M. N.; Dickter, A. H.; Teichmann, L.; Kidder, A.; Corriveau, A.; Zheng, C.; Vaziri-Pashkam, M.; Baker, C. I.: THINGS-fMRI/MEG: A densely sampled multimodal neuroimaging dataset of brain responses to a broad range of natural object images. Society for Neuroscience 50th Annual Meeting, Virtual (2021)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data. 10th IMPRS NeuroCom Summer School, Virtual (2021)

Contier, O.; Hebart, M. N.; Dickter, A. H.; Teichmann, L.; Kidder, A.; Corriveau, A.; Zheng, C.; Vaziri-Pashkam, M.; Baker, C. I.: THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images. Vision Sciences Society Annual Meeting (V-VSS), Virtual (2021)