Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language
Hosseini, E., & Fedorenko, E. 2023. Advances in Neural Information Processing Systems, 36, 43918–43930. DOI: 10.48550/arXiv.2311.04930.
PDF | Supplementary Information | tweeprint
WhisBERT: Multimodal text-audio language modeling on 100M words
Wolf, L., Tuckute, G., Kotar, K., Hosseini, E., Regev, T., Wilcox, E., & Warstadt, A. 2023. Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, 2 (BabyLM Challenge), 253-258.
PDF
Let’s move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision
DiCarlo, J.J., Yamins, D.L., Ferguson, M.E., Fedorenko, E., Bethge, M., Bonnen, T., & Schrimpf, M. 2023. Behavioral and Brain Sciences, 46, e390. DOI: 10.1017/S0140525X23001607. PMID: 38054303.
PDF
Event knowledge in large language models: The gap between the impossible and the unlikely
Kauf, C.*, Ivanova, A. A.*, Rambelli, J., Chersoni, E., She, J. S., Chowdhury, Z., Fedorenko, E. & Lenci, A. 2023. Cognitive Science, 47(11), p.e13386. DOI: 10.1111/cogs.13386. PMID: 38009752.
PDF | Supplementary Information | tweeprint
The language network is not engaged in object categorization
Benn, Y.*, Ivanova, A. A.*, Clark, O., Mineroff, Z., Seikus, C., Silva, J. S., Varley, R.^ & Fedorenko, E.^ 2023. Cerebral Cortex, 33(19), 10380–10400. 10.1093/cercor/bhad289. PMID: 37557910. PMC10545444.
PDF | Supplementary Information | tweeprint
Lexical semantic content, not syntactic structure, is the main contributor to ANN-brain similarity of fMRI responses in the language network
Kauf, C.*, Tuckute, G.*, Levy, R., Andreas, J., & Fedorenko E. 2023. Neurobiology of Language, 5(1), 7-42. DOI: 10.1162/nol_a_00116. PMID: 38645614. PMC11025651.
PDF | Supplementary Information | tweeprint
Non-literal language processing is jointly supported by the language and Theory of Mind networks: Evidence from a novel meta-analytic fMRI approach
Hauptman, M., Blank, I.^, & Fedorenko, E.^ 2023. Cortex, 162, 96-114. DOI: 10.1016/j.cortex.2023.01.013. PMID: 37023480. PMC10210011.
PDF | Supplementary Information | tweeprint
The human language system, including its inferior frontal component in “Broca’s area”, does not support music perception
Chen, X., Affourtit, J., Ryskin, R., Regev, T. I., Norman-Haignere, S., Jouravlev, O., Malik-Moraleda, S., Kean, H., Varley, R.^ & Fedorenko, E.^ 2023. Cerebral Cortex, 33(12), 7904-7929. DOI: 10.1093/cercor/bhad087. PMID: 37005063. PMC10505454.
PDF | Supplementary Information | tweeprint
No evidence of theory of mind reasoning in the human language network
Shain, C.*, Paunov, A.*, Chen, X.*, Lipkin, B., & Fedorenko, E. 2023. Cerebral Cortex, 33(10) 6299-6319. DOI: 10.1093/cercor/bhac505. PMID: 36585774. PMC10183748.
PDF | Supplementary Information | tweeprint
Mahowald Fedorenko (2016) Online Supplement: Large-scale fMRI datasets of functional ‘localizers’ for the language and Multiple Demand networks extend the evidence for reliable individual …
Lipkin, B., Affourtit, J., Small, H., Mineroff, Z., Nieto-Castañón & Fedorenko, E. 2023. DOI: 10.6084/m9.figshare.22183564.
Lipkin et al. (2022) Online Supplement: Probabilistic atlases for the multiple demand (MD) and theory of mind (ToM) networks based on large-scale precision localizers
Lipkin, B., Blank, I. & Fedorenko, E. 2023. DOI: 10.6084/m9.figshare.22306348.