Online abstract. One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examine judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative, and that listeners use Bayesian inference to recover speakers' intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that using information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication. One of the most astonishing features of human language is its ability to convey information efficiently in context. Each utterance need not carry every detail; instead, listeners can infer speakers' intended meanings by assuming utterances convey only relevant information. These communicative inferences rely on the shared assumption that speakers are informative, but not more so than is necessary given the communicators' common knowledge and the task at hand. Many theories provide high-level accounts of these kinds of inferences (1-3), yet-perhaps be-1
REF proposed a rational speaker-listener model 1 https://github.com/mcao610/ProfileREG based on the assumptions that speakers attempt to be informative and that listeners use Bayesian inference to recover the speakers' intended referents.
Predicting pragmatic reasoning in language games
Science
Medicine Computer Science