Surprisal Theory

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16 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Estimating Surprisal from the Speech Signal
Oct 13, 2026Zachary Hopton, Samuel Kiegeland, Francesco Ignazio Re +4Surprisal Theory Computational Psycholinguistics
University of Zurich · ETH Zürich · CHI-FRO +1
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics
Oct 13, 2026Zachary Hopton, Francesco Ignazio Re, Samuel Kiegeland +3Computational Psycholinguistics Surprisal Theory
University of Zurich · ETH Zürich · CHI-FRO +1
A Systematic Analysis of the Predictive Power of LM Surprisal in Reading Chinese
Oct 4, 2026Hongao Zhu, Muxiaoqiao Xu, Yikang Liu +4Language Model Scaling Laws Language Modeling
Department of Linguistics, UC San Diego · School of Foreign Languages, Shanghai Jiao Tong University · School of Computer Science, Shanghai Jiao Tong University +3
Distinct dynamics of conceptual and referential disruptions in human reading and large language model processing
Aug 26, 2026Rui He, Nihal Altay, Wolfram HinzenSurprisal Theory Language Modeling
Grammar and Cognition Lab, Department of Translation & Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain · Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
From Exposure to Expectation: Frequency, Surprisal, and Language Across Development in Spanish
Aug 23, 2026Francisco Portillo LópezSurprisal Theory Child Language Acquisition
Universidad de Navarra, Pamplona, Spain
Surprisal Theory is Tautological (without Rational Grounding)
Jul 23, 2026Ryan CotterellSurprisal Theory Cognitive Modeling
ETH Zürich
surprisal is Not a Theory
Jul 22, 2026Andrés Buxó-Lugo, Aniello De Santo, Morgan Grobol +2Surprisal Theory Language Modeling
aUniversity at Buffalo, Buffalo, NY, USA · bUniversity of Utah, Salt Lake City, UT, USA · cUniversité Paris Nanterre, Paris, France +1
Contextual Semantic Relevance and Word Surprisal Predict N400 and P600 Dynamics During Naturalistic Reading
Jul 5, 2026Kun Sun, Rong WangComputational Psycholinguistics Neural Encoding
Tongji University · University of Tuebingen
Trajectory Dynamics in Language Model Hidden States Predict Human Processing Costs Beyond Surprisal
Jun 3, 2026Elan BarenholtzSurprisal Theory Computational Psycholinguistics
Machine Perception & Cognitive Robotics Laboratory Department of Psychology / Center for Complex Systems Florida Atlantic University
Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis
May 14, 2026William Timkey, Brian Dillon, Tal LinzenSurprisal Theory Language Modeling
Department of Linguistics, New York University, 10 Washington Pl, New York, NY 10003, USA · Department of Linguistics, University of Massachusetts Amherst, 650 N Pleasant St, Amherst, MA 01002, USA · Center for Data Science, New York University, 60 5th Ave, New York, NY 10011, USA
The Frequency Confound in Language-Model Surprisal and Metaphor Novelty
May 7, 2026Omar Momen, Sina ZarrießLanguage Modeling Computational Psycholinguistics
CRC 1646 – Linguistic Creativity in Communication · Bielefeld University, Germany
On the Proper Treatment of Units in Surprisal Theory
Apr 30, 2026Samuel Kiegeland, Vésteinn Snæbjarnarson, Tim Vieira +1Surprisal Theory Computational Psycholinguistics
ETH Zürich · CHI-FRO · University of Copenhagen
An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal
Apr 20, 2026Ryo Yoshida, Shinnosuke Isono, Taiga Someya +2Computational Psycholinguistics Surprisal Theory
♠The University of Tokyo · ♡NINJAL · ♢NII LLMC +1
Expect the Unexpected? Testing the Surprisal of Salient Entities
Apr 12, 2026Jessica Lin, Amir ZeldesSurprisal Theory Computational Psycholinguistics
Department of Linguistics, Georgetown University
Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths
Mar 24, 2026Amani Maina-Kilaas, Roger LevySurprisal Theory Computational Psycholinguistics
Department of Brain and Cognitive Sciences, MIT
Extending Minimal Pairs with Ordinal Surprisal Curves and Entropy Across Applied Domains
Mar 15, 2026Andrew KatzLLM Evaluation Surprisal Theory
Department of Engineering Education, Virginia Tech

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Estimating Surprisal from the Speech Signal

Zachary Hopton, Samuel Kiegeland, Francesco Ignazio Re, Patrick Haller+3

University of Zurich · ETH Zürich · CHI-FRO+1

Surprisal Theory Computational Psycholinguistics Speech Language Models

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Although language is predominantly spoken, empirical tests of surprisal theory rely almost entirely on written text. In practice, surprisal is estimated using language models (LMs) trained on text and applied to transcriptions, creating a text bottleneck that limits evaluation of the theory in the spoken modality. We remove this bottleneck by estimating surprisal directly from the speech signal using a speech LM. This raises a key question: should surprisal be defined over realization-specific acoustic inputs or over abstract linguistic units? Since a single unit can have many distinct acoustic realizations, each assigned a different probability by the model, defining surprisal over abstract units is difficult. To address this, we introduce an importance sampling estimator that approximates the probability of an abstract unit by marginalizing over realizations generated by a text-to-speech model. Using pupillometry data from a listening study, we evaluate surprisal theory in this text-free setting at multiple levels of granularity. Obtaining surprisal from several speech LMs, we consistently find that surprisal estimated via importance sampling better predicts pupil dilation than realization-specific surprisal for words. We also find that surprisal values for sub-lexical units are predictive of pupil dilation. While we find that surprisal can be estimated without recourse to text, and that marginalizing over distinct realizations of words is more predictive of lexical processing effort, surprisal as a whole is not a strong predictor of change in pupil size, regardless of its source.