ArticleFrontiers in psychology2021
Neurobehavioral Correlates of Surprisal in Language Comprehension: A Neurocomputational Model.
Article in Frontiers in psychology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Prediction in reading: A review of predictability effects, their theoretical implications, and beyond.Psychonomic bulletin & review · 2025Review
- What's Surprising About Surprisal.Computational brain & behavior · 2025Article
- Invariants for neural automata.Cognitive neurodynamics · 2024Article
- Single-trial neurodynamics reveal N400 and P600 coupling in language comprehension.Cognitive neurodynamics · 2024Article
- Tracking Lexical and Semantic Prediction Error Underlying the N400 Using Artificial Neural Network Models of Sentence Processing.Neurobiology of language (Cambridge, Mass.) · 2024Article
- On the Mathematical Relationship Between Contextual Probability and N400 Amplitude.Open mind : discoveries in cognitive science · 2024Article
- Fame through surprise: How fame-seeking mass shooters diversify their attacks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Investigating a neural language model's replicability of psycholinguistic experiments: A case study of NPI licensing.Frontiers in psychology · 2023Article
- Is the Mind Inherently Predicting? Exploring Forward and Backward Looking in Language Processing.Cognitive science · 2022Article
- Native Word Order Processing Is Not Uniform: An ERP Study of Verb-Second Word Order.Frontiers in psychology · 2022Article
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4 authors.
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Abstract
Expectation-based theories of language comprehension, in particular Surprisal Theory, go a long way in accounting for the behavioral correlates of word-by-word processing difficulty, such as reading times. An open question, however, is in which component(s) of the Event-Related brain Potential (ERP) signal Surprisal is reflected, and how these electrophysiological correlates relate to behavioral processing indices. Here, we address this question by instantiating an explicit neurocomputational model of incremental, word-by-word language comprehension that produces estimates of the N400 and the P600-the two most salient ERP components for language processing-as well as estimates of "comprehension-centric" Surprisal for each word in a sentence. We derive model predictions for a recent experimental design that directly investigates "world-knowledge"-induced Surprisal. By relating these predictions to both empirical electrophysiological and behavioral results, we establish a close link between Surprisal, as indexed by reading times, and the P600 component of the ERP signal. The resultant model thus offers an integrated neurobehavioral account of processing difficulty in language comprehension.
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