Lune

EMNLP Findings2023

The Linearity of the Effect of Surprisal on Reading Times across Languages

Weijie Xu, Jason Chon, Tianran Liu, Richard Futrell

Abstract

In psycholinguistics, surprisal theory posits that the amount of online processing effort expended by a human comprehender per word positively correlates with the surprisal of that word given its preceding context. In addition to this overall correlation, more importantly, the specific quantitative form taken by the processing effort as a function of surprisal offers insights into the underlying cognitive mechanisms of language processing. Focusing on English, previous studies have looked into the linearity of surprisal on reading times. Here, we extend the investigation by examining eyetracking corpora of seven languages: Danish, Dutch, English, German, Japanese, Mandarin, and Russian. We find evidence for superlinearity in some languages, but the results are highly sensitive to which language model is used to estimate surprisal.

Ask Lune

Ask anything. The answer quotes the paper.

Lune reads all of it and the papers around it, then shows you the passages behind every answer.

Questions to start from

Lune answers from

the full paper · 6 top-tier papers that cite it · related work

Get the answer

Free to start. No credit card required.

Cited by top-tier papers6

  1. The Impact of Token Granularity on the Predictive Power of Language Model SurprisalByung-Doh Oh, William SchulerACL 2025 · 7 citations
  2. The Harmonic Structure of Information ContoursEleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu et al.ACL 2025 · 6 citations
  3. Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze SurprisalSathvik Nair, Byung-Doh OhACL 2026 · 1 citation
  4. Genre Matters: How Text Types Interact with Decoding Strategies and Lexical Predictors in Shaping Reading BehaviorLena Sophia Bolliger, Lena Ann JägerEMNLP 2025
  5. Memory efficiency and resource-rational encoding in sentence processingWeijie Xu, Brian Dillon, Richard FutrellACL 2026

Builds on2

  1. Revisiting the Uniform Information Density HypothesisClara Meister, Tiago Pimentel, Patrick Haller, Lena A. Jäger et al.EMNLP 2021 · 4 citations
  2. Lower Perplexity is Not Always Human-LikeTatsuki Kuribayashi, Yohei Oseki, Takumi Ito, Ryo Yoshida et al.ACL 2021

Related papers

  1. Probing for Reading TimesEleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re, Tianyang Xu et al.ACL 2026
  2. An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via SurprisalRyo Yoshida, Shinnosuke Isono, Taiga Someya, Yohei Oseki et al.ACL 2026
  3. Modeling Nonnative Sentence Processing with L2 Language ModelsTatsuya Aoyama, Nathan SchneiderEMNLP 2024 · 1 citation
  4. Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differencesPatrick Haller, Lena S. Bolliger, Lena Ann JägerACL Findings 2024 · 7 citations
  5. A surprisal-duration trade-off across and within the world's languagesTiago Pimentel, Clara Meister, Elizabeth Salesky, Simone Teufel et al.EMNLP 2021 · 2 citations
Dusk over the sea between two cliffs drawn in fine vertical lines