Publikationen

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

Muttenthaler, L.; Hebart, M. N.: THINGSvision: A Python toolbox for streamlining the extraction of activations from deep neural networks. Frontiers in Neuroinformatics 15, 679838 (2021)

Liu, P.; Chrysidou, A.; Doehler, J.; Hebart, M. N.; Wolbers, T.; Kuehn, E.: The organizational principles of de-differentiated topographic maps in somatosensory cortex. eLife 10, e60090 (2021)

Hebart, M. N.; Zheng, C. Y.; Pereira, F.; Baker, C. I.: Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature Human Behaviour 4 (11), S. 1173 - 1185 (2020)

Hebart, M. N.; Schuck, N. W.: Current topics in computational cognitive neuroscience. Neuropsychologia 147, 107621 (2020)

Kaniuth, P.; Hebart, M. N.: Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. In Journal of Vision, 20, 1076. Vision Sciences Society Annual Meeting 2020, 19. Juni 2020 - 24. Juni 2020. Scholar One, Inc., Charlottesville, VA (2020)

Hebart, M. N.; Hannsen, H.: 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.: Revealing the multidimensional representation of objects in behavior and language. Meeting, Department of Experimental Psychology, University of Giessen, Germany (2021)

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

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

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)

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)

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)

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)

Hebart, M. N.; Contier, O.; Teichmann, L.; Rockter, A.H.; Zheng, C.Y.; Kidder, A.; Corriveau, A.; Vaziri-Pashkam, M.; Baker, C. I.: THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior. (2022)