Proceedings of the Annual Meeting of the Cognitive Science Society

Speakers often refer to context only implicitly when using lan-guage. The utterance “it’s warm outside” could signal it’swarm relative to other days of the year or just relative to thecurrent season (e.g., it’s warm for winter). Warm vaguely con-veys that the temperature is high relative to some contextualcomparison class, but little is known about how a listener de-cides upon such a standard of comparison. Here, we formalizehow world knowledge and listeners’ internal models of speechproduction can drive the resolution of a comparison class incontext. We introduce a Rational Speech Act model and de-rive two novel predictions from it, which we validate using aparaphrase experiment to measure listeners’ beliefs about thelikely comparison class used by a speaker. Our model makesquantitative predictions given prior world knowledge for thedomains in question. We triangulate this knowledge with afollow-up language task in the same domains, using Bayesiandata analysis to infer priors from both data sets