This animation illustrates the progress of a Quest session. The sequence of trial values and outcomes are shown by the green (correct) and red (incorrect) dots. The gray region is the Bayesian posterior probability density for threshold.
This figure summarizes data collected with the Quest session. The sequence of trial values and outcomes are shown by the green (correct) and red (incorrect) dots. The blue bars are the histogram of the distribution of trials over values. The gray region is the Bayesian posterior probability density for threshold. The blue dots are the percent correct, and the vertical gray lines are binomial 95% confidence limits. The curve is the maximum likelihood version of a Weibull psychometric function. The legend indicates estimated parameters and error of this function.
Psychophysica is a collection of Mathematica Notebooks containing functions for collecting and analyzing data in psychophysical experiments. The components at this time are Quest.nb and Psychometrica.nb, and SimulateQuest.nb. These are described briefly below. In each case the user with a properly configured browser and a copy of Mathematica or the free Wolfram CDF Player can examine these notebooks online or offline. Each includes a brief tutorial.
Psychometrica.nb contains functions for fitting and plotting psychometric data, as well as definitions of LogWeibull, Logistic, and Normal psychometric functions.
Quest is an adaptive psychometric procedure for use in psychophysical experiments. The motivation for adaptive methods, the basis of the Quest algorithm, and some descriptions of alternative methods are avaliable in the publications listed at the end of this document.
SimulateQuest contains functions to simulate the Quest procedure with a simulated observer. This is useful for tutorial purposes, and to understand the efficiency of the procedure under various conditions.
Watson, A. B., & Solomon, J. A. (1997). Psychophysica: Mathematica notebooks for psychophysical experiments. Spatial Vision, 10(4), 447-466.
The method of constant stimuli is inefficient (1990) Andrew B. Watson & Fitzhugh, A., Perception & Psychophysics 47(1), 87-91.
QUEST: A Bayesian adaptive psychometric method (1983) Andrew B. Watson & Denis G. Pelli, Perception and Psychophysics 33(2), 113-120.
Probability summation over time (1979) Andrew B. Watson, Vision Research 19, 515-522.
Emerson, P. L. (1986). Observations on maximum likelihood and Bayesian methods of forced-choice sequential threshold estimation. Perception & Psychophysics 39, 151-153.
King-Smith, P. E., Grigsby, S. S., Vingrys, A. J., Benes, S. C. & Supowit, A. (1994). Efficient and unbiased modifications of the QUEST threshold method: Theory, simulations, experimental evaluation, and practical implementation. Vision Research 34(7), 885-912.
Treutwein, B. (1995). Adaptive psychophysical procedures. Vision Research 35(17), 2503-2522.