ArticleComputational brain & behavior2025
What's Surprising About Surprisal.
Article in Computational brain & behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Measuring surprisal in sound sequences.Behavior research methods · 2026Article
- Executive resources shape the impact of language predictability across the adult lifespan.eLife · 2026Article
- Agreement Errors Disrupt Eye Movements in Reading Only When They Are Detected.Open mind : discoveries in cognitive science · 2026Article
- Large Language Models as Distributional Baselines for Language Tasks.Open mind : discoveries in cognitive science · 2026Article
- Lexical Surprisal Shapes the Time Course of Syntactic Structure Building.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Intraoperative cortical localization of music and language reveals signatures of structural complexity in posterior temporal cortex.iScience · 2023Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
In the computational and experimental psycholinguistic literature, the mechanisms behind syntactic structure building (e.g., combining words into phrases and sentences) are the subject of considerable debate. Much experimental work has shown that surprisal is a good predictor of human behavioral and neural data. These findings have led some authors to model language comprehension in a purely probabilistic way. In this paper, we use simulation to exemplify why surprisal works so well to model human data and to illustrate why exclusive reliance on it can be problematic for the development of mechanistic theories of language comprehension, particularly those with emphasis on meaning composition. Rather than arguing for the importance of structural or probabilistic information to the exclusion or exhaustion of the other, we argue more emphasis should be placed on understanding how the brain leverages both types of information (viz., statistical and structured). We propose that probabilistic information is an important Supplementary Information: The online version contains supplementary material available at 10.1007/s42113-025-00237-9.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.