@InProceedings{gauthier2023neural, author = {Gauthier, Jon and Levy, Roger}, title = {The neural dynamics of auditory word recognition and integration}, booktitle = {Proceedings of the 7th Conference on Cognitive Computational Neuroscience}, month = {August}, year = {2023}, address = {Oxford, England} }

@inproceedings{martin23_interspeech, author={Kinan Martin and Jon Gauthier and Canaan Breiss and Roger Levy}, title=, year=2023, booktitle={Proc. INTERSPEECH 2023}, pages={251--255}, doi={10.21437/Interspeech.2023-2359} }

@inproceedings{sinha-etal-2023-language, title = "Language model acceptability judgements are not always robust to context", author = "Sinha, Koustuv and Gauthier, Jon and Mueller, Aaron and Misra, Kanishka and Fuentes, Keren and Levy, Roger and Williams, Adina", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.333", pages = "6043--6063", abstract = "Targeted syntactic evaluations of language models ask whether models show stable preferences for syntactically acceptable content over minimal-pair unacceptable inputs. Our best syntactic evaluation datasets, however, provide substantially less linguistic context than models receive during pretraining. This mismatch raises an important question: how robust are models{'} syntactic judgements across different contexts? In this paper, we vary the input contexts based on: length, the types of syntactic phenomena it contains, and whether or not there are grammatical violations. We find that model judgements are generally robust when placed in randomly sampled linguistic contexts, but are unstable when contexts match the test stimuli in syntactic structure. Among all tested models (GPT-2 and five variants of OPT), we find that model performance is affected when we provided contexts with matching syntactic structure: performance significantly improves when contexts are acceptable, and it significantly declines when they are unacceptable. This effect is amplified by the length of the context, except for unrelated inputs. We show that these changes in model performance are not explainable by acceptability-preserving syntactic perturbations. This sensitivity to highly specific syntactic features of the context can only be explained by the models{'} implicit in-context learning abilities.", }

@inproceedings{hu2020systematic, author = {Hu, Jennifer and Gauthier, Jon and Qian, Peng and Wilcox, Ethan and Levy, Roger}, title = {A systematic assessment of syntactic generalization in neural language models}, booktitle = {Proceedings of the Association of Computational Linguistics}, year = {2020} }

@article{gauthier2019from, title={From mental representations to neural codes: A multilevel approach}, volume={42}, DOI={10.1017/S0140525X19001390}, journal={Behavioral and Brain Sciences}, publisher={Cambridge University Press}, author={Gauthier, Jon and Loula, João and Pollock, Eli and Wilson, Tyler Brooke and Wong, Catherine}, year={2019}, pages={e228} }

@InProceedings{gauthier2019linking, author = {Gauthier, Jon and Levy, Roger P.}, title = {Linking human and artificial neural representations of language}, booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing}, month = {November}, year = {2019}, address = {Hong Kong}, publisher = {Association for Computational Linguistics}, }

@InProceedings{gauthier2019rational, author = {Gauthier, Jon and Levy, Roger and Tenenbaum, Joshua B.}, title = {A rational model of syntactic bootstrapping}, booktitle = {Proceedings of the 41st Annual Meeting of the Cognitive Science Society}, month = {July}, year = {2019}, address = {Montreal, Canada}, }

@InProceedings{gauthier2018word, author = {Gauthier, Jon and Ivanova, Anna}, title = {Does the brain represent words? An evaluation of brain decoding studies of language understanding.}, booktitle = {Proceedings of the 2nd Conference on Cognitive Computational Neuroscience}, month = {September}, year = {2018}, address = {Philadelphia, Pennsylvania} },

@InProceedings{gauthier2018word, author = {Gauthier, Jon and Levy, Roger and Tenenbaum, Joshua B.}, title = {Word learning and the acquisition of syntactic--semantic overhypotheses}, booktitle = {Proceedings of the 40th Annual Meeting of the Cognitive Science Society}, month = {July}, year = {2018}, address = {Madison, Wisconsin}, }

@misc{1705.11168, Author = {Li Lucy and Jon Gauthier}, Title = {Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning}, Year = {2017}, Eprint = {arXiv:1705.11168}, }

@misc{1610.03585, Author = {Jon Gauthier and Igor Mordatch}, Title = {A Paradigm for Situated and Goal-Driven Language Learning}, Year = {2016}, Eprint = {arXiv:1610.03585}, }

@InProceedings{bowman2016fast, author = {Bowman, Samuel R. and Gauthier, Jon and Rastogi, Abhinav and Gupta, Raghav and Manning, Christopher D. and Potts, Christopher}, title = {A Fast Unified Model for Parsing and Sentence Understanding}, booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = {August}, year = {2016}, address = {Berlin, Germany}, publisher = {Association for Computational Linguistics}, pages = {1466--1477}, url = {http://www.aclweb.org/anthology/P16-1139} }

@TechnicalReport{gauthier2015conditional, author = {Gauthier, Jon}, title = {Conditional generative adversarial networks for convolutional face generation}, year = {2015} }