Quick Methods: Bayesian Adaptive Methods for Estimating Psychological Functions

Zhong-Lin Lu

Abstract

Adaptive procedures are developed to reduce the burden of data collection in psychophysics by creating more efficient experimental test designs and methods of estimating either statistics or parameters. In some cases, these adaptive procedures may reduce the amount of testing by as much as 80% to 90%. For example, adaptive methods for estimating properties of psychometric functions improve test efficiency by targeting stimuli to pre-specified regions of the empirical psychometric functions (e.g. threshold region) based on subject responses. Our goal is to develop adaptive methods for the estimation of psychophysically measured functions and surfaces. In this talk, I will describe the Bayesian adaptive framework for optimizing psychophysical tests and its application to the development of various quick methods for measuring TvC functions, d' psychometric functions, contrast sensitivity functions, and forgetting functions. I will provide animations, simulations and psychophysical validations of these methods, and discuss challenges and future directions.