fMRI reveals language-specific predictive coding during naturalistic sentence comprehension C Shain, IA Blank, M van Schijndel, W Schuler, E Fedorenko Neuropsychologia 138, 107307, 2020 | 174 | 2020 |
Incremental language comprehension difficulty predicts activity in the language network but not the multiple demand network L Wehbe, IA Blank, C Shain, R Futrell, R Levy, T von der Malsburg, ... Cerebral Cortex 31 (9), 4006-4023, 2021 | 61 | 2021 |
Large-scale evidence for logarithmic effects of word predictability on reading time C Shain, C Meister, T Pimentel, R Cotterell, R Levy Proceedings of the National Academy of Sciences 121 (10), e2307876121, 2024 | 56 | 2024 |
Memory access during incremental sentence processing causes reading time latency C Shain, M Van Schijndel, R Futrell, E Gibson, W Schuler Proceedings of the workshop on computational linguistics for linguistic …, 2016 | 52 | 2016 |
The synchrony and diachrony of differential object marking in Paraguayan Guaraní C Shain, J Tonhauser Language Variation and Change 22 (3), 321-346, 2010 | 42 | 2010 |
Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex C Shain, IA Blank, E Fedorenko, E Gibson, W Schuler Journal of Neuroscience 42 (39), 7412-7430, 2022 | 39 | 2022 |
Breaking NLP: Using morphosyntax, semantics, pragmatics and world knowledge to fool sentiment analysis systems T Mahler, W Cheung, M Elsner, D King, MC de Marneffe, C Shain, ... Proceedings of the First Workshop on Building Linguistically Generalizable …, 2017 | 32 | 2017 |
A large-scale study of the effects of word frequency and predictability in naturalistic reading C Shain Proceedings of the 2019 conference of the north American chapter of the …, 2019 | 31 | 2019 |
Similarity of computations across domains does not imply shared implementation: the case of language comprehension E Fedorenko, C Shain Current Directions in Psychological Science 30 (6), 526-534, 2021 | 27 | 2021 |
Continuous-time deconvolutional regression for psycholinguistic modeling C Shain, W Schuler Cognition 215, 104735, 2021 | 27 | 2021 |
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders C Shain, M Elsner Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 21 | 2019 |
Distributed sensitivity to syntax and semantics throughout the language network C Shain, H Kean, C Casto, B Lipkin, J Affourtit, M Siegelman, F Mollica, ... Journal of Cognitive Neuroscience 36 (7), 1427-1471, 2024 | 20* | 2024 |
No evidence of theory of mind reasoning in the human language network C Shain, A Paunov, X Chen, B Lipkin, E Fedorenko Cerebral Cortex 33 (10), 6299-6319, 2023 | 20 | 2023 |
Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute? M Elsner, AD Sims, A Erdmann, A Hernandez, E Jaffe, L Jin, ... Journal of Language Modelling 7 (1), 53-98, 2019 | 18 | 2019 |
Deconvolutional time series regression: A technique for modeling temporally diffuse effects C Shain, W Schuler Proceedings of the 2018 conference on empirical methods in natural language …, 2018 | 18 | 2018 |
Speech segmentation with a neural encoder model of working memory M Elsner, C Shain Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 17 | 2017 |
The distribution of differential object marking in Paraguayan Guaraní C Shain Ohio State University, 2009 | 14 | 2009 |
Acquiring language from speech by learning to remember and predict C Shain, M Elsner Proceedings of the 24th Conference on Computational Natural Language …, 2020 | 13 | 2020 |
Coreference and focus in reading times E Jaffe, C Shain, W Schuler Proceedings of the 8th Workshop on Cognitive Modeling and Computational …, 2018 | 13 | 2018 |
CDRNN: Discovering complex dynamics in human language processing C Shain Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 10 | 2021 |