Decomposition of surprisal: Unified computational model of ERP
components in language processing

Decomposition of surprisal: Unified computational model of ERP components in language processing

10 September 2024

Richard Futrell

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Papers citing "Decomposition of surprisal: Unified computational model of ERP components in language processing"

7 / 7 papers shown

Title
Testing the Predictions of Surprisal Theory in 11 Languages Ethan Gotlieb Wilcox Tiago Pimentel Clara Meister Ryan Cotterell R. Levy LRM 88 69 0 07 Jul 2023
So Cloze yet so Far: N400 Amplitude is Better Predicted by Distributional Information than Human Predictability Judgements J. Michaelov S. Coulson Benjamin Bergen 55 44 0 02 Sep 2021
Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude? J. Michaelov Megan D. Bardolph S. Coulson Benjamin Bergen 48 23 0 20 Jul 2021
How well does surprisal explain N400 amplitude under different experimental conditions? J. Michaelov Benjamin Bergen 38 41 0 09 Oct 2020
On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior Ethan Gotlieb Wilcox Jon Gauthier Jennifer Hu Peng Qian R. Levy 45 169 0 02 Jun 2020
Language Models are Few-Shot Learners Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan ... Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever Dario Amodei BDL 798 42,055 0 28 May 2020
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance R. Thomas McCoy Junghyun Min Tal Linzen 96 150 0 07 Nov 2019