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Warm (for Winter): Inferring Comparison Classes in Communication

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Cognitive Science - 2022 - Tessler - Warm for Winter Inferring Comparison Classes in Communication.pdf

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sword-2022-05-24T13:23:45.original.xml (130 B)
Original SWORD entry document
Author(s)
Tessler, Michael Henry
•
Goodman, Noah D
Date Issued
March 2022
Journal
Cognitive Science
Publisher
Wiley
Citation
Tessler, Michael Henry and Goodman, Noah D. 2022. "Warm (for Winter): Inferring Comparison Classes in Communication." Cognitive Science, 46 (3).
Version
Final published version
Abstract
The meanings of natural language utterances depend heavily on context. Yet, what counts as context is often only implicit in conversation. The utterance it's warm outside signals that the temperature outside is relatively high, but the temperature could be high relative to a number of different comparison classes: other days of the year, other weeks, other seasons, etc. Theories of context sensitivity in language agree that the comparison class is a crucial variable for understanding meaning, but little is known about how a listener decides upon the comparison class. Using the case study of gradable adjectives (e.g., warm), we extend a Bayesian model of pragmatic inference to reason flexibly about the comparison class and test its qualitative predictions in a large-scale free-production experiment. We find that human listeners infer the comparison class by reasoning about the kinds of observations that would be remarkable enough for a speaker to mention, given the speaker and listener's shared knowledge of the world. Further, we quantitatively synthesize the model and data using Bayesian data analysis, which reveals that usage frequency and a preference for basic-level categories are two main factors in comparison class inference. This work presents new data and reveals the mechanisms by which human listeners recover the relevant aspects of context when understanding language.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International License
https://creativecommons.org/licenses/by/4.0
Persistent DSpace Link
https://hdl.handle.net/1721.1/142669
DOI of Published Version
https://doi.org/10.1111/cogs.13095
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