Publications of Martin N. Hebart

Roth, J., Seeliger, K., Schmid, T., & Hebart, M. N. Preferred stimuli for individual voxels in the human visual system. Poster presented at the 2021 Computational and Systems Neuroscience (Cosyne), Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-B680-6

Kaniuth, P., & Hebart, M. N. Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data? Poster presented at the Society for Neuroscience Global Connectome, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000A-E2F4-3

Singer, J., Seeliger, K., & Hebart, M. N. The representation of object drawings and sketches in deep convolutional neural networks. Poster presented at the NeurIPS Workshop SVRHM, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-3686-1

Kaniuth, P., & Hebart, M. N. Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. Poster presented at the Vision Sciences Society Annual Meeting 2020, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000A-E2EE-B

Kaemmer, L., Kroell, L. M., Knapen, T., Rolfs, M., & Hebart, M. N. (2025, April 22). Feedback of peripheral saccade targets to early foveal cortex. BioRxiv. doi:10.1101/2025.02.20.639262

Teichmann, L., Hebart, M. N., & Baker, C. I. (2025, February 28). Dynamic representation of multidimensional object properties in the human brain. BioRxiv. doi:10.1101/2023.09.08.556679

Kaniuth, P., Mahner, F., Perkuhn, J., & Hebart, M. N. (2024, December 22). A high-throughput approach for the efficient prediction of perceived similarity of natural objects. BioRxiv. doi:10.1101/2024.06.28.601184

Amme, C., Sulewski, P., Spaak, E., Hebart, M. N., König, P., & Kietzmann, T. C. (2024, October 26). Saccade onset, not fixation onset, best explains early sensory responses across the human visual cortex during naturalistic vision. BioRxiv. doi:10.1101/2024.10.25.620167

Hofmann, S., Ciston, A., Koushik, A., Klotzsche, F., Hebart, M. N., Müller, K.-R., … Gaebler, M. (2024, September 16). Human-aligned deep and sparse encoding models of dynamic 3D face similarity perception. PsyArXiv. doi:10.31234/osf.io/f62pw

Contier, O., Baker, C. I., & Hebart, M. N. (2024, July 11). Distributed representations of behavior-derived object dimensions in the human visual system. BioRxiv. doi:10.1101/2023.08.23.553812

Muttenthaler, L., Zheng, C. Y., McClure, P., Vandermeulen, R. A., Hebart, M. N., & Pereira, F. (2022, May 30). VICE: Variational Interpretable Concept Embeddings. ArXiv. doi:10.48550/arXiv.2205.00756

Zheng, C. Y., Pereira, F., Baker, C. I., & Hebart, M. N. (2019, January 9). Revealing interpretable object representations from human behavior. ArXiv. Retrieved from http://hdl.handle.net/21.11116/0000-0005-3920-7

Görgen, K., Hebart, M. N., Allefeld , C., & Haynes, J.-D. (2017, March 20). The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods. ArXiv. Retrieved from http://hdl.handle.net/21.11116/0000-0005-20DB-0