@inproceedings{hopton-etal-2026-revisiting,
title = "Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics",
author = "Hopton, Zachary and
Re, Francesco Ignazio and
Kiegeland, Samuel and
Opedal, Andreas and
Chodroff, Eleanor and
Cotterell, Ryan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2399/",
pages = "54526--54554",
ISBN = "979-8-89176-459-0",
abstract = "Contextual and unigram surprisal are standard predictors of reading time in computational psycholinguistics. While contextual surprisal is typically derived from neural language models, unigram surprisal is usually obtained from independent corpus counts. Although both quantities are deterministic functionals of a single underlying distribution{---}capturing conditional and marginal structure respectively{---}this practice breaks their probabilistic link and may introduce estimation error from the mismatch. We propose three principles for estimating unigram surprisal in psycholinguistic research, including the requirement that both quantities be derived from the same language model. We operationalize this principle by introducing a Monte Carlo estimator of unigram surprisal from a language model, along with a marginalized variant that has lower variance empirically. When unigram surprisal is re-estimated under this unified framework, we find evidence that predictors change in their predictive contribution to reading time. We also confirm prior findings that frequency can be a stronger predictor of processing effort than contextual surprisal under this unified framework."
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%0 Conference Proceedings
%T Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics
%A Hopton, Zachary
%A Re, Francesco Ignazio
%A Kiegeland, Samuel
%A Opedal, Andreas
%A Chodroff, Eleanor
%A Cotterell, Ryan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hopton-etal-2026-revisiting
%X Contextual and unigram surprisal are standard predictors of reading time in computational psycholinguistics. While contextual surprisal is typically derived from neural language models, unigram surprisal is usually obtained from independent corpus counts. Although both quantities are deterministic functionals of a single underlying distribution—capturing conditional and marginal structure respectively—this practice breaks their probabilistic link and may introduce estimation error from the mismatch. We propose three principles for estimating unigram surprisal in psycholinguistic research, including the requirement that both quantities be derived from the same language model. We operationalize this principle by introducing a Monte Carlo estimator of unigram surprisal from a language model, along with a marginalized variant that has lower variance empirically. When unigram surprisal is re-estimated under this unified framework, we find evidence that predictors change in their predictive contribution to reading time. We also confirm prior findings that frequency can be a stronger predictor of processing effort than contextual surprisal under this unified framework.
%U https://aclanthology.org/2026.emnlp-main.2399/
%P 54526-54554
Markdown (Informal)
[Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics](https://aclanthology.org/2026.emnlp-main.2399/) (Hopton et al., EMNLP 2026)
ACL