Bayesian cognitive science

Bayesian Cognitive Science is a rapidly growing approach to cognitive science concerned with rational models, in the particular sense of "Bayesian rationality".

This work often consists of testing the hypothesis that cognitive systems behave like rational Bayesian agents in particular types of tasks (past work has applied this idea to categorization, language, motor control, sequence learning, reinforcement learning, theory of mind. At other times, Bayesian rationality is assumed, and the goal is to infer the knowledge that agents have, and the mental representations that they use.

It is important to contrast this with the ordinary use of Bayesian inference in cognitive science, which is independent of rational modeling (see e.g. Michael Lee's work).

See also

• Active inference
• Bayesian approaches to brain function
• Bayesian programming

References

• John R. Anderson (1990) "The Adaptive Character of Thought". Lawrence Erlbaum Associates
• T. L. Griffiths, and J. B. Tenenbaum (2006) "Optimal Predictions in Everyday Cognition" Psychological Science 17(9), 767-773.

• M. Oaksford and N. Chater (1999) - "Rational Models of Cognition"