Human visual and parietal cortex encode visual choices independent of motor plans. - Martin N. Hebart; Tobias H. Donner; John-Dylan Haynes (NeuroImage, 2012)

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data. - Martin N. Hebart; Kai Görgen; John-Dylan Haynes (Frontiers Neuroinformatics, 2014)

Parietal and early visual cortices encode working memory content across mental transformations. - Thomas B. Christophel; Radoslaw Martin Cichy; Martin N. Hebart; John-Dylan Haynes (NeuroImage, 2015)

Memory detection using fMRI - Does the encoding context matter? - Judith Peth; Tobias Sommer; Martin N. Hebart; Gerhard Vossel; Christian Büchel; Matthias Gamer (NeuroImage, 2015)

Analyzing neuroimaging data with subclasses: A shrinkage approach. - Johannes Höhne; Daniel Bartz; Martin N. Hebart; Klaus-Robert Müller; Benjamin Blankertz (NeuroImage, 2016)

The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks. - B. B. Bankson; Martin N. Hebart; Iris I. A. Groen; Chris I. Baker (NeuroImage, 2018)

The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods. - Kai Görgen; Martin N. Hebart; Carsten Allefeld; John-Dylan Haynes (NeuroImage, 2018)

Deconstructing multivariate decoding for the study of brain function. - Martin N. Hebart; Chris I. Baker (NeuroImage, 2018)

Revealing interpretable object representations from human behavior. - Charles Y. Zheng; Francisco Pereira 001; Chris I. Baker; Martin N. Hebart (ICLR (Poster), 2019)

THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks. - Lukas Muttenthaler; Martin N. Hebart (Frontiers Neuroinformatics, 2021)

Semantic features of object concepts generated with GPT-3. - Hannes Hansen; Martin N. Hebart (CogSci, 2022)

VICE: Variational Interpretable Concept Embeddings. - Lukas Muttenthaler; Charles Y. Zheng; Patrick McClure; Robert A. Vandermeulen; Martin N. Hebart; Francisco Pereira 001 (NeurIPS, 2022)

Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. - Philipp Kaniuth; Martin N. Hebart (NeuroImage, 2022)

Revealing interpretable object representations from human visual cortex and artificial neural networks. - Martin N. Hebart (BCI, 2023)

Getting aligned on representational alignment. - Ilia Sucholutsky; Lukas Muttenthaler; Adrian Weller; Andi Peng; Andreea Bobu; Been Kim; Bradley C. Love; Erin Grant; Jascha Achterberg; Joshua B. Tenenbaum; Katherine M. Collins; Katherine L. Hermann; et al. (CoRR, 2023)

Dimensions underlying the representational alignment of deep neural networks with humans. - Florian P. Mahner; Lukas Muttenthaler; Umut Güçlü; Martin N. Hebart (CoRR, 2024)

What comparing deep neural networks can teach us about human vision. - Katja Seeliger; Martin N. Hebart (Nat. Mac. Intell., 2024)

CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience. - Marie St-Laurent; Basile Pinsard; Oliver Contier; Elizabeth DuPre; Katja Seeliger; Valentina Borghesani; Julie A. Boyle; Lune Bellec; Martin N. Hebart (CoRR, 2025)