Comprehenders' Error Correction Mechanisms Are Finely Calibrated to Language Production Statistics.
- 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Abstract
An essential feature of human communication is robustness to errors: even when speakers and writers make mistakes, listeners and readers can usually determine the intended meaning. A leading hypothesis is that comprehenders achieve robust error correction by internally deploying a "noisy-channel" model of the language production process. Crucially, to best achieve error robustness, a comprehender's noisy-channel model should be finely calibrated to the environmental statistics of language production, incorporating not only prior expectations about communicative intents but also structure-sensitive likelihoods of different types of speaker error. This environmentally calibrated noisy-channel hypothesis has been difficult to test, because the rates of most types of language production errors are hard to estimate. Here, we test this hypothesis by experimentally investigating comprehenders' interpretations of subject-verb agreement errors, for which context-contingent language production error rates are feasible to estimate. In a novel free-form error correction task, participants edited sentences with subject-verb agreement mismatches to indicate what they think was intended. Bayesian analysis of the resulting data shows that comprehenders' interpretations reflect both item-specific prior expectations and structure-sensitive error likelihoods that closely correspond to error rates in language production. These results provide quantitative evidence that humans closely track prior statistics over linguistic forms and deploy a noisy-channel model finely calibrated to statistics of the linguistic environment to achieve robust language understanding.
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BibTeX
@article{qian2026comprehenders,
title = {{Comprehenders' Error Correction Mechanisms Are Finely Calibrated to Language Production Statistics.}},
author = {Peng Qian and Roger Levy},
journal = {Cognitive science},
year = {2026},
month = oct,
doi = {10.1111/cogs.70277},
url = {https://europepmc.org/article/MED/42961108},
note = {CC BY},
}
RIS
TY - JOUR
TI - Comprehenders' Error Correction Mechanisms Are Finely Calibrated to Language Production Statistics.
AU - Peng Qian
AU - Roger Levy
PY - 2026
DA - 2026/10/30
JO - Cognitive science
DO - 10.1111/cogs.70277
UR - https://europepmc.org/article/MED/42961108
AB - An essential feature of human communication is robustness to errors: even when speakers and writers make mistakes, listeners and readers can usually determine the intended meaning. A leading hypothesis is that comprehenders achieve robust error correction by internally deploying a "noisy-channel" model of the language production process. Crucially, to best achieve error robustness, a comprehender's noisy-channel model should be finely calibrated to the environmental statistics of language production, incorporating not only prior expectations about communicative intents but also structure-sensitive likelihoods of different types of speaker error. This environmentally calibrated noisy-channel hypothesis has been difficult to test, because the rates of most types of language production errors are hard to estimate. Here, we test this hypothesis by experimentally investigating comprehenders' interpretations of subject-verb agreement errors, for which context-contingent language production error rates are feasible to estimate. In a novel free-form error correction task, participants edited sentences with subject-verb agreement mismatches to indicate what they think was intended. Bayesian analysis of the resulting data shows that comprehenders' interpretations reflect both item-specific prior expectations and structure-sensitive error likelihoods that closely correspond to error rates in language production. These results provide quantitative evidence that humans closely track prior statistics over linguistic forms and deploy a noisy-channel model finely calibrated to statistics of the linguistic environment to achieve robust language understanding.
SN - 0364-0213
SN - 1551-6709
ER -