Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Roelfsema Group, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands (2023)

Hebart, M. N.: Revealing the core dimensions underlying mental representations of objects. CRC Workshop on Cardinal Mechanisms of Perception, Rauischholzhausen Castle, Germany (2022)

Hansen, H.; Hebart, M. N.: Semantic features of object concepts generated with GPT-3. 44th Annual Conference of the Cognitive Science Society (CogSci), Toronto, ON, Canada (2022)

Hebart, M. N.: Core representational dimensions of visually-perceived objects. Mind Brain Annual Meeting (SAMBA), Salzburg, Austria (2022)

Hebart, M. N.: The THINGS initiative: A global initiative of researchers for representative sampling of objects in brains, behavior, and computational models. Annual Meeting of the Vision Science Society (VSS) , St. Pete Beach, FL, USA (2022)

Hebart, M. N.; Perkuhn, J.; Kaniuth, P.: Efficiently-generated object similarity scores predicted from human feature ratings and deep neural network activations. Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2022)

Muttenthaler, L.: Interpretable object dimensions in humans and deep convolutional neural networks. Meeting, TU Berlin, Germany (2021)

Seeliger, K.: Convolutional neural networks and visual information processing. Osnabrück Search Symposium Computational Neuroscience , 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)

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)