Peak Information Flux: A Resource Rational Measure of Language Processing Difficulty

Millan Hamilton, Jiaxuan Li


Abstract
A key goal in psycholinguistics is to quantify real-time language processing effort. A notable amount of literature links the processing effort to the amount of unexpected information carried by the new input. However, such theories fail to capture the dynamic evolution of comprehenders’ interpretation which may involve revision and operate under cognitive constraints. We propose peak information flux, a metric that better captures the cost of reanalysis through a resource rational framework. We characterize language comprehension as continuous belief updating under finite information processing capacity and that the maximal rate of belief change signals language processing effort. We validate the performance of peak information flux on Syntactic Ambiguity Processing and Natural Stories corpora featuring self-paced reading time and a variety of eye-tracking metrics representing different stages of lexical processing. Peak information flux predicts reading behavior across multiple measures and complements surprisal in measures associated with reinterpretation and recovery. Additional analyses reveal that garden-path constructions exhibit delayed and asymmetric information flux dynamics, supporting the hypothesis of a sustained probabilistic revision process. Our model provides an information-theoretic metric for language comprehension under resource constraints.
Anthology ID:
2026.findings-emnlp.1662
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2026
Month:
October
Year:
2026
Address:
Budapest, Hungary
Editors:
Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
35428–35441
Language:
URL:
https://aclanthology.org/2026.findings-emnlp.1662/
DOI:
10.18653/2026.findings-emnlp.1662
Bibkey:
Cite (ACL):
Millan Hamilton and Jiaxuan Li. 2026. Peak Information Flux: A Resource Rational Measure of Language Processing Difficulty. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 35428–35441, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):
Peak Information Flux: A Resource Rational Measure of Language Processing Difficulty (Hamilton & Li, Findings 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.findings-emnlp.1662.pdf