Experimentation is at the core of research in the behavioral sciences, yet observations can be expensive and time-consuming to acquire (e.g., fMRI scans, responses from infant participants). A major interest of researchers is designing experiments that lead to maximal accumulation of information about the phenomenon under study with the fewest possible number of measurements. In addressing this challenge, statisticians have developed optimal experimental design (OED) methods. In this talk I will introduce a hierarchical Bayesian extension of OED that provides a judicious way to exploit two complementary schemes of inference (with past and current data) to achieve even greater accuracy and efficiency in information gain. The new method is demonstrated and validated through simulations and experiments in the area of adaptive vision testing, specifically in the problem of estimating the contrast sensitivity of human vision.