STOPPING RULE DETERMINATION FOR GREEN'S MAXIMUM-LIKELIHOOD ADAPTIVE PROCEDURE WITH PSYCHOGEAR LIBRARY

This paper presents a case study aimed at identifying the optimal stopping rules for theGreens (1993) Maximum Likelihood adaptive procedure for psychophysics threshold esti-mation, involving a Monte Carlo simulation. Although this family of adaptive procedure iswidely involved in perceptual experiments, there are almost few criteria over a correct andaccepted stopping rule. The goal of this work is to identify the optimal stopping rule andits dependence by the minimization function parameters. The simulation runs startingfrom the capabilities of PsychoGear, a new library of psychophysics methods that sup-plements visual, audio, and haptics stimuli. The functionalities of PsychoGear let us toeasily identify the optimal stopping rule for the Green's procedure. The not trivial MonteCarlo experimental setup is implemented in a clear code which can easily reused in furtherexperiments and simulations.