Abstract
QUEST+ is a Bayesian adaptive psychometric testing method that allows an arbitrary number of stimulus dimensions, psychometric function parameters, and trial outcomes. It is a generalization and extension of the original QUEST procedure and incorporates many subsequent developments in the area of parametric adaptive testing. With a single procedure, it is possible to implement a wide variety of experimental designs, including conventional threshold measurement; measurement of psychometric function parameters, such as slope and lapse; estimation of the contrast sensitivity function; measurement of increment threshold functions; measurement of noise-masking functions; Thurstone scale estimation using pair comparisons; and categorical ratings on linear and circular stimulus dimensions. QUEST+ provides a general method to accelerate data collection in many areas of cognitive and perceptual science.
Highlights
We introduce a general method for efficient adaptive data collection in a broad range of psychometric experiments
QUEST is an adaptive testing procedure for estimating threshold from a sequence of psychophysical trials (Watson & Pelli, 1979, 1983). It assumes a single stimulus dimension and two possible trial outcomes, and it estimates a single psychometric function parameter that is defined on the stimulus dimension
The categories could be integer numerical ratings as one example. This situation has been addressed with multiple QUEST staircases, one for each criterion, but here we show it can be done with a single QUESTþ procedure
Summary
We introduce a general method for efficient adaptive data collection in a broad range of psychometric experiments. QUEST is an adaptive testing procedure for estimating threshold from a sequence of psychophysical trials (Watson & Pelli, 1979, 1983) It assumes a single stimulus dimension and two possible trial outcomes, and it estimates a single psychometric function parameter that is defined on the stimulus dimension. Expanding the number of stimulus dimensions has been explored by Kujala and Lukka (2006), Vul, Bergsma, and MacLeod (2010); and DiMattina (2015) These efforts have used ML or Bayesian estimation and entropy minimization for trial placement but have pursued complex or less general techniques to accelerate the procedure.
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