Hebart, M. N.: Revealing interpretable representations in artificial and biological vision. Japanese Meeting for Human Brain Imaging, National Institute for Physiological Sciences, Okazaki, Japan (2021)

Seeliger, K.: A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time. Symposium on Naturalistic Stimuli in Cognitive Neuroscience, Virtual (2021)

Hebart, M. N.: Revealing the similarities and differences between object representations in humans and DNNs. Tagung experimentell arbeitender Psychologen (TeaP), Ulm, Germany (2021)

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. NeurIPS workshop “Shared Visual Representations in Human & Machine Intelligence”, Virtual (2020)

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)

Herholz, P.; Gulban, O. F.; Dockès, J.; Cruces, R.; Royer, J.; Poline, J.-B.: Large-scale & multimodal perspectives on auditory cortex organization. 29th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Montréal, QC, Canada (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)

Siying, X.; Kaiser, D.; Singer, J.; Cichy, R.: The effects of visual backward masking on visual spatiotemporal dynamics. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (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)

Singer, J.; Karapetian, A.; Cichy, R.: Revealing the locus and content of behaviorally relevant information about real-world scenes in human visual. 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)