Publications

Le, L.; Ambrogioni, L.; Seeliger, K.; Güçlütürk, Y.; van Gerven, M.; Güçlü, U.: Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity. Frontiers in Neuroscience 16, 940972 (2022)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. NeuroImage 257, 119294 (2022)

Singer, J.; Seeliger, K.; Kietzmann, T. C.; Hebart, M. N.: From photos to sketches-how humans and deep neural networks process objects across different levels of visual abstraction. Journal of Vision 22 (2), 4 (2022)

Grootswagers, T.; Zhou, I.; Robinson, A. K.; Hebart, M. N.; Carlson, T. A.: Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams. Scientific Data 9 (1), 3 (2022)

Hansen, H.; Hebart, M. N.: Semantic features of object concepts generated with GPT-3. In: Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 44. (2022)

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)

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)

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