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)

Seeliger, K.: Synthesizing preferred stimuli for individual voxels in the human visual system. Vision Sciences Society (V-VSS), Virtual (2021)

Roth, J.; Seeliger, K.; Schmid, T.; Hebart, M. N.: Preferred stimuli for individual voxels in the human visual system. 2021 Computational and Systems Neuroscience (Cosyne), Virtual (2021)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data? Society for Neuroscience Global Connectome, Virtual (2021)

Singer, J.; Seeliger, K.; Hebart, M. N.: The representation of object drawings and sketches in deep convolutional neural networks. NeurIPS Workshop SVRHM, Virtual (2020)

Kaniuth, P.; Hebart, M. N.: Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. Vision Sciences Society Annual Meeting 2020, Virtual (2020)

Teichmann, L.; Hebart, M. N.; Baker, C. I.: Multidimensional object properties are dynamically represented in the human brain. (2023)

Contier, O.; Baker, C. I.; Hebart, M. N.: Distributed representations of behaviorally relevant object dimensions in the human visual system. (2023)

Singer, J. J. D.; Karapetian, A.; Hebart, M. N.; Cichy, R. M.: The link between visual representations and behavior in human scene perception. (2023)

Schmidt, F.; Hebart, M. N.; Schmid, A. a. C.; Fleming, R. W.: Core dimensions of human material perception. (2022)

Dima, D. C.; Hebart, M. N.; Isik, L.: A data-driven investigation of human action representations. (2022)

Stoinski, L. M.; 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. (2022)

Muttenthaler, L.; Zheng, C. Y.; McClure, P.; Vandermeulen, R. A.; Hebart, M. N.; Pereira, F.: VICE: Variational Interpretable Concept Embeddings. (2022)

Kramer, M. A.; Hebart, M. N.; Baker, C. I.; Bainbridge, W. A.: The features underlying the memorability of objects. (2022)