We present a computational Bayesian approach for Wiener diffusion models, which are prominent accounts of response time distributions in decision-making. We first develop a general closed-form analytic approximation to the response time distributions for one-dimensional diffusion processes, and derive the required Wiener diffusion as a special case. We use this result to undertake Bayesian modeling of benchmark data, using posterior sampling to draw inferences about the interesting psychological parameters. With the aid of the benchmark data, we show the Bayesian account has several advantages, including dealing naturally with the parameter variation needed to account for some key features of the data, and providing quantitative measures to guide decisions about model construction.

A theory of symmetric phase shift keying is developed using quantum mechanics. The minimum average probability of error is then determined for phase-shift-keyed optical communication systems which use physical states. It is shown that this minimum error is achieved by using states which are represented in the number state basis by discrete prolate spheroidal sequences.>