Readers make targeted regressions to plausible errors in reanalysis of "noisy-channel garden-path'' sentences

Thomas Hikaru Clark, Roger P. Levy, Edward Gibson

Keywords: Psycholinguistics, Noisy-Channel Processing, Reading

TL;DR: Readers make targeted reading regressions to the locations of likely errors in noisy-channel garden-path sentences

Abstract:

A key question in psycholinguistics is how inferences about the meaning of linguistic input unfold incrementally a comprehender's mind.
In this work, we study reading dynamics for ``noisy-channel garden-path'' sentences, which temporarily appear well-formed but feature late-appearing violations of expectation that can be resolved not by inferring an alternative syntactic structure, but by inferring the presence of an error.
We find evidence for targeted regressions -- eye movements towards regions that are promising loci of possible errors in light of later-arriving information, showing patterns consistent with the posterior inferences of a model of noisy-channel processing with reanalysis.
We discuss the implications of these findings for theories of noisy-channel language comprehension and information-theoretic explanations of reading dynamics.

Scope Confirmation: To the best of my judgment, this submission falls within the scope of CoNLL.

Primary Area Selection: Computational Psycholinguistics, Cognition and Linguistics

Use Of Generative Artificial Intelligence Tools: Yes, for writing code

Data Collection From Human Subjects: Yes, with details included in the main paper or in an appendix on (1) how the data was obtained (2) how participants were recruited and paid (3) how consent was obtained (4) whether a IRB protocol was approved for this study. Note that providing this information is obligatory.

Submission Type: Archival: I certify that the submission has not been previously published, nor is the material in it under review by another journal or conference. Further, no material in it will be submitted for review at another conference or journal while under review by CoNLL 2026.

Submission Number: 166