Project Publications
  1. Aubret, A., Schaumlöffel, T., Roig, G., & Triesch, J. (2024). Learning Object Semantic Similarity with Self-Supervision. arXiv. https://doi.org/10.48550/arXiv.2405.05143
  2. Dwivedi, K., Sadiya, S., Balode, M. P., Roig, G., & Cichy, R. M. (2024). Visual features are processed before navigational affordances in the human brain. Scientific Reports, 14(1), 5573. https://doi.org/10.1038/s41598-024-55652-y
  3. Ernst, M. R., López, F. M., Aubret, A., Fleming, R. W., & Triesch, J. (2024). Self-Supervised Learning of Color Constancy. arXiv. http://arxiv.org/abs/2404.08127
  4. Oota, S. R., Çelik, E., Deniz, F., & Toneva, M. (2024). Speech language models lack important brain-relevant semantics. arXiv. https://doi.org/10.48550/arXiv.2311.04664
  5. Nicholls, V. I., Krugliak, A., Alsbury-Nealy, B., Gramann, K., & Clarke, A. (2024). Congruency effects on object recognition persist when objects are placed in the wild: An AR and mobile EEG study. bioRxiv. https://doi.org/10.1101/2024.05.30.596613
  6. Vilas, M. G., Adolfi, F., Poeppel, D., & Roig, G. (2024). Position Paper: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience. arXiv. https://doi.org/10.48550/arXiv.2406.01352
  7. Vilas, M. G., Schaumlöffel, T., & Roig, G. (2023). Analyzing Vision Transformers for Image Classification in Class Embedding Space. arXiv. http://arxiv.org/abs/2310.18969
  8. Oota, S. R., Gupta, M., & Toneva, M. (2023). Joint processing of linguistic properties in brains and language models. arXiv. http://arxiv.org/abs/2212.08094
  9. Schaumlöffel, T., Aubret, A., Roig, G., & Triesch, J. (2023). Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play. 2023 IEEE International Conference on Development and Learning (ICDL), 67–72. https://doi.org/10.1109/ICDL55364.2023.10364409
  10. Schaumlöffel, T., Vilas, M. G., & Roig, G. (2023). PEACS: PREFIX ENCODING FOR AUDITORY CAPTION SYNTHESIS. IEEE Transactions on Multimedia, 17(10), 1733–1746. https://doi.org/10.1109/TMM.2015.2428998
  11. Sassenhagen, J., & Fiebach, C. J. (2020). Traces of Meaning Itself: Encoding Distributional Word Vectors in Brain Activity. Neurobiology of Language, 1(1), 54–76. https://doi.org/10.1162/nol_a_00003

Background Publications
  1. Bersch, D., Dwivedi, K., Vilas, M., Cichy, R. M., & Roig, G. (2022). Net2Brain: A Toolbox to compare artificial vision models with human brain responses. arXiv. https://doi.org/10.48550/arXiv.2208.09677
  2. Toneva, M., Mitchell, T. M., & Wehbe, L. (2022). Combining computational controls with natural text reveals aspects of meaning composition. Nature Computational Science, 2(11), 745–757. https://doi.org/10.1038/s43588-022-00354-6
  3. Dwivedi, K., Bonner, M. F., Cichy, R. M., & Roig, G. (2021). Unveiling functions of the visual cortex using task-specific deep neural networks. PLOS Computational Biology, 17(8), e1009267. https://doi.org/10.1371/journal.pcbi.1009267
  4. Dwivedi, K., Cichy, R. M., & Roig, G. (2021). Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks. Journal of Cognitive Neuroscience, 33(10), 2032–2043. https://doi.org/10.1162/jocn_a_01624
  5. Sassenhagen, J., & Fiebach, C. J. (2020). Traces of Meaning Itself: Encoding Distributional Word Vectors in Brain Activity. Neurobiology of Language, 1(1), 54–76. https://doi.org/10.1162/nol_a_00003
  6. Toneva, M., & Wehbe, L. (2019). Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). In arXiv.org. https://arxiv.org/abs/1905.11833v4
  7. Schwartz, D., Toneva, M., & Wehbe, L. (2019). Inducing brain-relevant bias in natural language processing models. arXiv. https://doi.org/10.48550/arXiv.1911.03268