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)

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)

Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Neural Coding Lab (Umut Güçlü), Donders Institute, Radboud University, Nijmegen, the Netherlands (virtual) (2023)

Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Bradley Love Lab, Division of Psychology and Language Sciences, University College London, United Kingdom (2023)

Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Retreat of Neural Dynamics of Visual Cognition Lab (Radoslaw Cichy, FU Berlin), Eberswalde, Germany (2023)

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)