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Updated: Jun 12, 2025
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Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
Published on: October 18, 2018
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Language models outperform cloze predictability in a cognitive model of reading
Adrielli Tina Lopes Rego1, Joshua Snell2, Martijn Meeter1
1Department of Education, Vrije Universiteit Amsterdam, and LEARN! Research Institute, Amsterdam, The Netherlands.
Plos Computational Biology
|September 25, 2024
Summary
Large language models (LLMs) offer a better measure of word predictability in reading than traditional cloze tasks. This improves computational models of reading and eye-movement control.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Psycholinguistics
Background:
- Word predictability is crucial for reading, but current theories lack sophisticated explanations.
- Computational reading models often use cloze norms, but their precise meaning is unclear.
Purpose of the Study:
- To investigate if large language models (LLMs) can provide a more accurate measure of word predictability for reading models.
- To explore the interplay between word predictability and eye movements during reading.
Main Methods:
- Implemented a novel parallel-graded mechanism for contextual predictions in LLMs.
- Utilized OB1-reader, a cognitive model of reading, for simulations.
- Compared model fit to eye-movement data using predictability values from cloze tasks versus LLMs (GPT-2, LLaMA).
Main Results:
- LLM-derived predictability values provided a better fit to human eye-movement data than cloze norms.
- Root Mean Square Error was lower when using LLM predictability.
Conclusions:
- LLMs can enhance cognitive reading models by incorporating higher-order language processing.
- LLM predictability offers a more precise proxy for word predictability in computational models of reading.
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