Sequential Bayesian updating as a model for human perception

Sequential Bayesian updating has been proposed as model for explaining various systematic biases in human perception, such as the central tendency, range effects, and serial dependence. The present chapter introduces to the principal ideas behind Bayesian updating for the random-change model introduced previously and shows how to implement sequential updating using the exact method via probability distributions, the Kalman filter for Gaussian distributions, and a particle filter for approximate sequential updating. Finally, it is demonstrated how to couple perception to action by selecting an appropriate action based on the posterior distribution that results from sequential updating.