ArticleOpen mind : discoveries in cognitive science2023
The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing.
Article in Open mind : discoveries in cognitive science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Eye movements reveal a dissociation between prediction and structural processing difficulty in language comprehension.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cognitive Processes of Probabilistic Prediction in Reading: Language Model Surprisal Across Model Sizes, Token Granularity, and Reading Paradigms.Journal of Intelligence · 2026Article
- Large Language Models as Distributional Baselines for Language Tasks.Open mind : discoveries in cognitive science · 2026Article
- Sentence processing by humans and machines: Large language models as a tool to better understand human reading.Psychonomic bulletin & review · 2025Review
- EMTeC: A corpus of eye movements on machine-generated texts.Behavior research methods · 2025Article
- Large-scale evidence for logarithmic effects of word predictability on reading time.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- On the Mathematical Relationship Between Contextual Probability and N400 Amplitude.Open mind : discoveries in cognitive science · 2024Article
- Word Frequency and Predictability Dissociate in Naturalistic Reading.Open mind : discoveries in cognitive science · 2024Article
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4 authors.
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Abstract
Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly predict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal-those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory (Hale, 2001; Levy, 2008a) which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms.
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