An adaptive psychophysical method for subject classification

Abstract

In psychophysical experiments, one's goal is usually to measure some continuous parameter hypothesized to determine the statistical properties of a subject's responses. Methods are well developed that adaptively manipulate stimulus characteristics in such a way that the reliability of the parameter estimate is maximized, However, such methods are inapplicable in situations in which the goal is to assign subjects to discrete categories, rather than to measure a continuous parameter, This paper introduces a technique that is directly applicable to efficient categorization and that adaptively manipulates stimulus characteristics in such a way that the information obtained from each trial is maximized. This technique is based on the principle of minimum estimated expected entropy, whereby stimulus parameters on each trial are chosen in order to minimize the estimated expected entropy of the a posteriori probability distribution that expresses how likely a subject is to belong to each of a group of mutually exclusive categories. A sample implementation of the technique-the classification of infant subjects according to their audiograms-is then described and evaluated via computer simulation.

qCSF in Clinical Application: Efficient Characterization and Classification of Contrast Sensitivity Functions in Amblyopia

“…After defining a probability distribution over the four CSF parameters, a one-step-ahead search of the stimulus space (over dimensions of spatial frequency and contrast) is used to choose stimuli that maximize the information gained over the CSF parameter space. 36,38,44,45 After the Bayesian update of the probability distribution following each trial, a CSF estimate can be calculated from the expected values of the four parameters.…”

qPR: An adaptive partial-report procedure based on Bayesian inference

“…Bayesian inference is an important computational method in statistics, computer technology, and many domains of science (Androutsopoulos et al, 2000 ; Dawid, 2005 ; Kruschke, 2011 ; Yang & Rannala, 1997 ). It has been increasingly applied in psychophysics (Cobo-Lewis, 1997 ; Kontsevich & Tyler, 1999 ; Kujala & Lukka, 2006 ; Lesmes et al, 2006 , 2010 ; Watson & Pelli, 1983 ) and clinical trials (Berry, Carlin, Lee, & Muller, 2010 ).…”

“…Here, information is quantified by entropy, a measure of uncertainty associated with variables (Shannon, 1948 ). The qPR uses a one-step-ahead search strategy to search for the cue delay that would lead to the minimum expected entropy (Cobo-Lewis, 1997 ; Kontsevich & Tyler, 1999 ; Kujala & Lukka, 2006 ; Lesmes, et al, 2006 ). It first computes the expected posterior probability distributions for all possible cue delays.…”

Functional Adaptive Sequential Testing

“…The principle of minimizing expected posterior entropy of the multimensional probability density function for the parameter values has been recently advocated as a method for stimulus placement (Cobo-Lewis, 1997;Kontsevich and Tyler, 1999;Kujala andLukka, 2006, Lesmes et al, 2006). This method is motivated by information theory and selects stimuli so as to maximize the certainty about parameter values one may have after the trial, thus maximizing the information gained from that trial.…”