ArticlePLoS computational biology2024
Language models outperform cloze predictability in a cognitive model of reading.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Munich Sentence (MuSe) Database: Completion norms and audio recordings for 619 German sentences.Behavior research methods · 2026Article
- All together now: Random Forests analysis reveals the joint impact of multiple statistical regularities on eye-movements during reading.Psychonomic bulletin & review · 2025Article
- Sentence processing by humans and machines: Large language models as a tool to better understand human reading.Psychonomic bulletin & review · 2025Review
- Quantifying word informativeness and its impact on eye-movement reading behavior: Cross-linguistic variability and individual differences.Behavior research methods · 2025Article
- On the spatial limits of parallel word processing in reading.Attention, perception & psychophysics · 2025Article
- Can Large Language Models Generate Useful Linguistic Corpora?: A Case Study of the Word Frequency Effect in Young German Readers.Open mind : discoveries in cognitive science · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Although word predictability is commonly considered an important factor in reading, sophisticated accounts of predictability in theories of reading are lacking. Computational models of reading traditionally use cloze norming as a proxy of word predictability, but what cloze norms precisely capture remains unclear. This study investigates whether large language models (LLMs) can fill this gap. Contextual predictions are implemented via a novel parallel-graded mechanism, where all predicted words at a given position are pre-activated as a function of contextual certainty, which varies dynamically as text processing unfolds. Through reading simulations with OB1-reader, a cognitive model of word recognition and eye-movement control in reading, we compare the model's fit to eye-movement data when using predictability values derived from a cloze task against those derived from LLMs (GPT-2 and LLaMA). Root Mean Square Error between simulated and human eye movements indicates that LLM predictability provides a better fit than cloze. This is the first study to use LLMs to augment a cognitive model of reading with higher-order language processing while proposing a mechanism on the interplay between word predictability and eye movements.
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Registered trials
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.