Publikationen von Martin N. Hebart

Hebart, M. N.; Donner, T. H.; Haynes, J.-D.: Human visual and parietal cortex encode visual choices independent of motor plans. NeuroImage 63 (3), S. 1393 - 1403 (2012)

Christophel, T. B.; Hebart, M. N.; Haynes, J.-D.: Decoding the contents of visual short-term memory from human visual and parietal cortex. The Journal of Neuroscience 32 (38), S. 12983 - 12989 (2012)

Hebart, M. N.; Hesselmann, G.: What visual information is processed in the human dorsal stream? The Journal of Neuroscience 32 (24), S. 8107 - 8109 (2012)

Stein, T.; Hebart, M. N.; Sterzer, P.: Breaking continuous flash suppression: A new measure of unconscious processing during interocular suppression? Frontiers in Human Neuroscience 5, 167 (2011)

Hesselmann, G.; Hebart, M. N.; Malach, R.: Differential BOLD activity associated with subjective and objective reports during “Blindsight” in normal observers. The Journal of Neuroscience 31 (36), S. 12936 - 12944 (2011)

Hebart, M. N.: Revealing interpretable object representations from human visual cortex and artificial neural networks. In: Proceedings of the 11th International Winter Conference on Brain-Computer Interface (BCI). IEEE (2023)

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, Bd. 44. (2022)

Singer, J.; Karapetian, A.; Hebart, M. N.; Cichy, R.: Revealing the locus and content of behaviorally relevant information about real-world scenes in human visual cortex. In Journal of Vision, 23, 4712. Scholar One, Inc., Charlottesville, VA (2023)

Schmidt, F.; Hebart, M. N.; Schmid, A.; Fleming, R. W.: The mental representation of materials distilled from >1.5 million similarity judgements. In Journal of Vision, 21, 1981. Scholar One, Inc., Charlottesville, VA (2021)

Seeliger, K.; Roth, J.; Schmid, T.; Hebart, M. N.: Synthesizing preferred stimuli for individual voxels in the human visual system. In Journal of Vision, 21, 2311. Scholar One, Inc., Charlottesville, VA (2021)

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.: 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)

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)

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)