@inproceedings{zhou-etal-2026-syntactic,
title = "Syntactic Belief Update as the Driver of Garden Path Processing Difficulty",
author = "Zhou, Alan and
Stanojevi{\'c}, Milo{\v{s}} and
Hale, John T.",
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.1805/",
pages = "41137--41156",
ISBN = "979-8-89176-459-0",
abstract = "Garden path sentences present a processing difficulty for humans{---} the sentence prefix leads the listener towards one interpretation, until the listener hears a critical word that shows that the initial interpretation was wrong. Lexical surprisal, a measure that usually predicts sentence processing difficulty quite well, fails to provide good predictions for garden path sentences.\par We propose an alternative that actively predicts a distribution over syntactic trees (its syntactic belief) and updates that distribution after each new word. If a processor is led down a garden path, syntactic beliefs will be wrong, requiring a large update at the critical word. The magnitude of the update is measured with a R{\'e}nyi divergence. Crucially, this metric is dependent on lexical items, but is distinct from lexical probability and measures a degree of belief update over non-lexical syntactic structure only. This Syntactic Belief Update provides a better fit to human reading time data on garden path sentences, suggesting a new research direction examining non-lexical alternatives to surprisal for psycholinguistics."
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<abstract>Garden path sentences present a processing difficulty for humans— the sentence prefix leads the listener towards one interpretation, until the listener hears a critical word that shows that the initial interpretation was wrong. Lexical surprisal, a measure that usually predicts sentence processing difficulty quite well, fails to provide good predictions for garden path sentences.\par We propose an alternative that actively predicts a distribution over syntactic trees (its syntactic belief) and updates that distribution after each new word. If a processor is led down a garden path, syntactic beliefs will be wrong, requiring a large update at the critical word. The magnitude of the update is measured with a Rényi divergence. Crucially, this metric is dependent on lexical items, but is distinct from lexical probability and measures a degree of belief update over non-lexical syntactic structure only. This Syntactic Belief Update provides a better fit to human reading time data on garden path sentences, suggesting a new research direction examining non-lexical alternatives to surprisal for psycholinguistics.</abstract>
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%0 Conference Proceedings
%T Syntactic Belief Update as the Driver of Garden Path Processing Difficulty
%A Zhou, Alan
%A Stanojević, Miloš
%A Hale, John T.
%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 zhou-etal-2026-syntactic
%X Garden path sentences present a processing difficulty for humans— the sentence prefix leads the listener towards one interpretation, until the listener hears a critical word that shows that the initial interpretation was wrong. Lexical surprisal, a measure that usually predicts sentence processing difficulty quite well, fails to provide good predictions for garden path sentences.\par We propose an alternative that actively predicts a distribution over syntactic trees (its syntactic belief) and updates that distribution after each new word. If a processor is led down a garden path, syntactic beliefs will be wrong, requiring a large update at the critical word. The magnitude of the update is measured with a Rényi divergence. Crucially, this metric is dependent on lexical items, but is distinct from lexical probability and measures a degree of belief update over non-lexical syntactic structure only. This Syntactic Belief Update provides a better fit to human reading time data on garden path sentences, suggesting a new research direction examining non-lexical alternatives to surprisal for psycholinguistics.
%U https://aclanthology.org/2026.emnlp-main.1805/
%P 41137-41156
Markdown (Informal)
[Syntactic Belief Update as the Driver of Garden Path Processing Difficulty](https://aclanthology.org/2026.emnlp-main.1805/) (Zhou et al., EMNLP 2026)
ACL