William Schuler
Computer Science · The Ohio State University
Publications
141
Citations
2,193
Est. group size
~2
Recurring co-author estimate
Active years
44
Publishing since 1983
William Schuler works on computational models of human language processing, focusing on how well large language models predict human reading times and brain activity (e.g., fMRI data). His recent work examines concepts like 'surprisal' (how unexpected a word is) and how factors such as model size, word frequency, and tokenization affect these predictions. This research bridges computational linguistics, psycholinguistics, and cognitive science.
Publication output has been steady to slightly growing over the last decade, rising from about 2 papers in 2017 to a sustained rate of roughly 7-9 per year since 2018.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly
Underline Science Inc. · 2026
- Evaluating Humanlike Memory Effects in Transformers Using Item Recognition Tasks
2026
- Dissociable frequency effects attenuate as large language model surprisal predictors improve
Journal of Memory and Language · 2025
- Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly
arXiv (Cornell University) · 2025
- Are Larger Language Models Better at Disambiguation?
2025
- The Impact of Token Granularity on the Predictive Power of Language Model Surprisal
2025
- The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage
2025
- The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage
arXiv (Cornell University) · 2025
- Vectors from Larger Language Models Predict Human Reading Time and fMRI Data More Poorly when Dimensionality Expansion is Controlled
arXiv (Cornell University) · 2025
- How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?
Open MIND · 2025
- How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?
2025
- A Deep Learning Approach to Analyzing Continuous-Time Cognitive Processes
Open Mind · 2024
- Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times
arXiv (Cornell University) · 2024
- Leading Whitespaces of Language Models’ Subword Vocabulary Pose a Confound for Calculating Word Probabilities
2024
- Frequency Explains the Inverse Correlation of Large Language Models’ Size, Training Data Amount, and Surprisal’s Fit to Reading Times
2024
- arXiv (Cornell University)×15
- OSF Preprints (OSF Preprints)×3
- Transactions of the Association for Computational Linguistics×2
- Medical Teacher×2
- Cognitive Science×2
- Sandra Kübler
Computer Science · Indiana University
- Michael White
Computer Science · The Ohio State University
- Micha Elsner
Computer Science · The Ohio State University
- Elsayed Issa
Computer Science · Purdue University West Lafayette
- Francis M. Tyers
Computer Science · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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