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Related Experiment Video

Updated: May 22, 2026

Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

Published on: July 22, 2025

Predicting pragmatic reasoning in language games.

Michael C Frank¹, Noah D Goodman

¹Department of Psychology, Stanford University, Stanford, CA 94305, USA. mcfrank@stanford.edu

Science (New York, N.Y.)

|May 26, 2012

Summary

This summary is machine-generated.

Human language efficiently conveys information through context. Bayesian inference and information theory offer precise predictions for pragmatic reasoning in communication, improving formal models.

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Area of Science:

Cognitive Science
Linguistics
Computational Neuroscience

Background:

Human language excels at efficient, context-dependent information transfer.
Existing theories of communicative inference lack precise, quantitative predictive power for pragmatic reasoning.

Purpose of the Study:

To develop and test a quantitative model of pragmatic reasoning in referential communication.
To assess the utility of information-theoretic tools and Bayesian inference in modeling human communication.

Main Methods:

Examined human judgments in simple referential communication games.
Modeled speaker informativeness and listener Bayesian inference for referent recovery.
Utilized information-theoretic principles to predict pragmatic reasoning.

Main Results:

The proposed model demonstrated a close, parameter-free fit to human judgments.
Bayesian inference effectively captures how listeners recover intended referents.
Information-theoretic principles accurately predict pragmatic reasoning patterns.

Conclusions:

Bayesian inference provides a robust framework for understanding pragmatic reasoning in human communication.
Information-theoretic tools are valuable for developing precise, quantitative models of communication.
This approach advances formal modeling of language and cognition.