Publications

Golan, T.; Taylor, J.; Schütt, H.; Peters, B.; Sommers, R. P.; Seeliger, K.; Doerig, A.; Linton, P.; Konkle, T.; van Gerven, M. et al.; Kording, K.; Richards, B.; Kietzmann, T. C.; Lindsay, G. W.; Kriegeskorte, N.: Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses. Behavioral and Brain Sciences 46, e392 (2023)

Kalyani, A.; Contier, O.; Klemm, L.; Azañon, E.; Schreiber, S.; Speck, O.; Reichert, C.; Kuehn, E.: Reduced dimension stimulus decoding and column-based modeling reveal architectural differences of primary somatosensory finger maps between younger and older adults. NeuroImage 283, 120430 (2023)

Doerig, A.; Sommers, R. P.; Seeliger, K.; Richards, B.; Ismael, J.; Lindsay, G. W.; Kording, K. P.; Konkle, T.; van Gerven, M. A. J.; Kriegeskorte, N. et al.; Kietzmann, T. C.: The neuroconnectionist research programme. Nature Reviews Neuroscience 24 (7), pp. 431 - 450 (2023)

Josephs, E. L.; Hebart, M. N.; Konkle, T.: Dimensions underlying human understanding of the reachable world. Cognition 234, 105368 (2023)

Kramer, M. A.; Hebart, M. N.; Baker, C. I.; Bainbridge, W. A.: The features underlying the memorability of objects. Science Advances 9 (17), eadd2981 (2023)

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 human brain and behavior. eLife 12, e82580 (2023)

Dima, D. C.; Hebart, M. N.; Isik, L.: A data-driven investigation of human action representations. Scientific Reports 13 (1), 5171 (2023)

Borghesani, V.; Armoza, J.; Hebart, M. N.; Bellec, P.; Brambati, S. M.: The Three Terms Task - An open benchmark to compare human and artificial semantic representations. Scientific Data 10 (1), 117 (2023)

Singer, J. J. D.; Cichy, R. M.; Hebart, M. N.: The spatiotemporal neural dynamics of object recognition for natural images and line drawings. The Journal of Neuroscience 43 (3), pp. 484 - 500 (2023)

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)

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)

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)

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)