The Influence of Prior Semantic Knowledge in Noisy Channel Interpretation
Name
chen-et-al-2025-the-influence-of-prior-semantic-knowledge-in-noisy-channel-interpretation.pdf
Description
Published version
Size
1.31 MB
Format
Adobe PDF
Checksum (MD5)
5c65e9bde2d4b2ad162b97233c836508
Author(s)
Chen, Sihan
Washington, Lia
Gibson, Edward
Date Issued
September 25, 2025
Journal
Quarterly Journal of Experimental Psychology
Publisher
SAGE Publications
Citation
Chen, S., Washington, L., & Gibson, E. (2025). The Influence of Prior Semantic Knowledge in Noisy Channel Interpretation. Quarterly Journal of Experimental Psychology, 0(0).
Version
Final published version
Abstract
How do comprehenders interpret semantically implausible sentences? Previous studies proposed a noisy-channel framework of sentence comprehension, where communication between a speaker and a comprehender happens in a noisy channel. The comprehender rationally adopts an interpretation of a sentence based on how likely the interpretation is (the semantic prior) and how likely is the interpretation corrupted into the perceived sentence because of noise (the likelihood). The theory predicted that comprehenders would be more likely to adopt a literal interpretation of an implausible sentence if their prior of implausible sentences were higher. To test this hypothesis, Gibson et al. manipulated the proportion of implausible test sentences in two sets of experiments, where participants read a number of sentences and answer a comprehension question following each sentence. Although their results supported the hypothesis, the experiment could be confounded (a) by participants’ adaptation effect (due to different experiment lengths) and (b) by different participants having different strategies to do the task (due to the between-subject design). In our study, we manipulated the semantic prior and controlled for these potential confounds. We found participants exposed to more implausible sentences were indeed more likely to interpret implausible sentences literally. Our results hence offer additional support for the noisy-channel framework.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/17470218251383526
