Publikationen

van Bree, S.; Poeppel, D.: Top-down and bottom-up neuroscience as collections of practices. Nature Reviews Neuroscience (2025)

Nortmann, T.; Sulewski, P.; Kietzmann, T. C.: Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision. Patterns (2025)

Muttenthaler, L.; Greff, K.; Born, F.; Spitzer, B.; Kornblith, S.; Mozer, M. C.; Müller, K.-R.; Unterthiner, T.; Lampinen, A. K.: Aligning machine and human visual representations across abstraction levels. Nature 647 (8089), S. 349 - 355 (2025)

Bockes, A.; Hebart, M. N.; Lingnau, A.: Revealing key dimensions underlying the recognition of dynamic human actions. Communications Psychology 3 (1), 149 (2025)

Kaemmer, L.; Kroell, L. M.; Knapen, T.; Rolfs, M.; Hebart, M. N.: Feedback of peripheral saccade targets to early foveal cortex. eLife (angenommen)

Mahner, F.; Muttenthaler, L.; Güçlü, U.; Hebart, M. N.: Dimensions underlying the representational alignment of deep neural networks with humans. Nature Machine Intelligence 7 (6), S. 848 - 859 (2025)

Gao, C.; Ajith, S.; Peelen, M. V.: Object representations drive emotion schemas across a large and diverse set of daily-life scenes. Communications Biology 8 (1), 697 (2025)

van Bree, S.; Levenstein, D.; Krause, M. R.; Voytek, B.; Gao, R.: Processes and measurements: A framework for understanding neural oscillations in field potentials. Trends in Cognitive Sciences 29 (5), S. 448 - 466 (2025)

Kaniuth, P.; Mahner, F.; Perkuhn, J.; Hebart, M. N.: A high-throughput approach for the efficient prediction of perceived similarity of natural objects. eLife (angenommen)

Roth, J.; Duan, Y.; Mahner, F.; Kaniuth, P.; Wallis, T. S. A.; Hebart, M. N.: Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience. Communications Psychology 3 (1), 62 (2025)

Hebart, M. N.: How modular are modules in visual cortex? Brain (2025)

Schmidt, F.; Hebart, M. N.; Schmid, A. C.; Fleming, R. W.: Core dimensions of human material perception. Proceedings of the National Academy of Sciences of the United States of America 122 (10), e2417202122 (2025)

Singer, J.; Karapetian, A.; Hebart, M. N.; Cichy, R. M.: Identifying and characterizing scene representations relevant for categorization behavior. Imaging Neuroscience 3, imag_a_00449 (2025)

Badwal, M. W.; Bergmann, J.; Roth, J.; Doeller, C. F.; Hebart, M. N.: The scope and limits of fine-grained image and category information in the ventral visual pathway. The Journal of Neuroscience 45 (3), e0936242024 (2025)

Contier, O.; Baker, C. I.; Hebart, M. N.: Distributed representations of behaviour-derived object dimensions in the human visual system. Nature Human Behaviour 8 (11), S. 2179 - 2193 (2024)

van Bree, S.; Mackenzie, A. S.; Wimber, M.: No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity. Imaging Neuroscience 2, S. 1 - 16 (2024)

van Dyck, L.; Bremmer, F.; Dobs, K.: Artificial intelligence meets body sense: Task-driven neural networks reveal computational principles of the proprioceptive pathway. Signal Transduction and Targeted Therapy 9 (1), 171 (2024)

van Bree, S.: Why the neural ingredients for a language of thought are not like spatial cells (commentary on Kazanina & Poeppel, 2023). European Journal of Neuroscience 59 (10), S. 2552 - 2555 (2024)

Stoinski, L. M.; Perkuhn, J.; Hebart, M. N.: THINGSplus: New norms and metadata for the THINGS database of 1854 object concepts and 26,107 natural object images. Behavior Research Methods 56 (3), S. 1583 - 1603 (2024)

Seeliger, K.; Hebart, M. N.: What comparing deep neural networks can teach us about human vision. Nature Machine Intelligence 6, S. 122 - 123 (2024)