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Synthese

, Volume 194, Issue 10, pp 3801–3836 | Cite as

Adjectival vagueness in a Bayesian model of interpretation

  • Daniel LassiterEmail author
  • Noah D. Goodman
S.I. : Vagueness and Probability

Abstract

We derive a probabilistic account of the vagueness and context-sensitivity of scalar adjectives from a Bayesian approach to communication and interpretation. We describe an iterated-reasoning architecture for pragmatic interpretation and illustrate it with a simple scalar implicature example. We then show how to enrich the apparatus to handle pragmatic reasoning about the values of free variables, explore its predictions about the interpretation of scalar adjectives, and show how this model implements Edgington’s (Analysis 2:193–204,1992, Keefe and Smith (eds.) Vagueness: a reader,  1997) account of the sorites paradox, with variations. The Bayesian approach has a number of explanatory virtues: in particular, it does not require any special-purpose machinery for handling vagueness, and it is integrated with a promising new approach to pragmatics and other areas of cognitive science.

Keywords

Vagueness Probability Cognitive science Sorites paradox 

Notes

Acknowledgments

Thanks to Michael Franke, Chris Potts, Chris Kennedy, Adrian Brasoveanu, Paul Égré, Alexis Wellwood, Lenhart Schubert, Richard Dietz, two Synthese reviewers, three SALT 23 reviewers, participants in our 2013 ESSLLI course “Probability in semantics and pragmatics”, participants in Lassiter’s 2014 NASSLLI course “Language understanding and Bayesian inference”, and audiences at SALT 23, Stanford, Northwestern, Brown, U. Chicago, and UT-Austin. This paper is modified and extended from Lassiter and Goodman (2013), which appeared in the proceedings of the conference Semantics & Linguistic Theory 23. This work was supported by a James S. McDonnell Foundation Scholar Award to NDG and by ONR Grant N00014-13-1-0788.

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Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA

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