Adjectival vagueness in a Bayesian model of interpretation

Daniel Lassiter*, Noah.D. Goodman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)3801-3836
Number of pages36
Issue number10
Early online date23 Jun 2015
Publication statusPublished - Oct 2017


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