Estimation of psychometric functions using a hybrid up–down maximum likelihood procedure

Abstract

Rapid, efficient, and reliable estimation of psychometric functions is a much sought after goal in psychophysics. Adaptive techniques of the up–down type have many advantages in this regard, but they are not without their limitations. An alternative approach is the use of maximum likelihood estimation in placing observations. Whereas the maximum likelihood method is as efficient if not more efficient than any other method of estimation, this property only holds for very large samples. The use of maximum likelihood estimation in adaptive testing has significant limitations when the sample size is very small. In this study, a hybrid adaptive procedure was developed using an up–down technique initially and then converting to a maximum likelihood procedure once sufficient samples have been obtained for reliable estimation using the latter technique. The up–down technique that is used reduces the step size systematically employing a rule analogous to that used in analog-to-digial converters and is referred to as the AD procedure. Monte Carlo simulations of experiments using the hybrid adaptive technique showed promising results in comparison with other adaptive procedures. [Research supported by Grant 5P50DC00178 from NIDCD.]