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A Comparison of Adaptive Psychometric Procedures Based on the Theory of Optimal Experiments and Bayesian Techniques: Implications for Cochlear Implant Testing

By Remus, Jeremiah J.; Collins, Leslie M. | Perception and Psychophysics, April 2007 | Go to article overview

A Comparison of Adaptive Psychometric Procedures Based on the Theory of Optimal Experiments and Bayesian Techniques: Implications for Cochlear Implant Testing


Remus, Jeremiah J., Collins, Leslie M., Perception and Psychophysics


Numerous previous studies have focused on the development of quick and efficient adaptive psychometric procedures. In psychophysics, there is often a model of the psychometric function supported by previous studies for the task of interest. The theory of optimal experiments provides a framework for utilizing a model of the process to develop quick and efficient sequential-testing strategies for estimating model parameters, making it appropriate for developing adaptive psychophysical-testing methods. In this study, we investigated the application of sequential parameter search strategies based on the theory of optimal experiments and Bayesian adaptive procedures for measuring psychophysical variables. The results presented in this article suggest that more sophisticated psychometric procedures can expedite the measurement of psychophysical variables. Such techniques for quickly collecting psychophysical data may be particularly useful in cochlear implant research, where a large set of psychophysical variables are useful for characterizing the performance of an implanted device. It is to be hoped that further development of these techniques will make psychophysical measurements available to clinicians for tuning and optimizing the speech processors of individual cochlear implant patients.

(ProQuest-CSA LLC: ... denotes formulae omitted.)

Development of more efficient psychometric procedures has been the focus of numerous published studies. Many of these techniques are adaptive, utilizing the outcomes of previous trials to determine the next step in the experiment. Pelli (1987) proposed the ideal psychometric procedure, looking ahead over every possible trial sequence for the length of the experiment to maximize confidence in the outcome. Several psychometric procedures-for example, QUEST (Watson & Pelli, 1983), ZEST (King-Smith, Grigsby, Vingrys, Benes, & Supowit, 1994), PSI (Kontsevich & Tyler, 1999)-utilize a Bayesian formulation to include the outcomes of previous trials. Studies have suggested that these techniques are highly efficient for measuring psychophysical variables.

One particular application for which efficient psychometric procedures are relevant is cochlear implant research. Psychophysical variables are the primary metric for gauging the operation and performance of a cochlear implant in vivo, since more invasive procedures are not feasible. However, a comprehensive assessment of cochlear implant performance requires collecting large amounts of psychophysical data in a time-consuming process. Hence, the motivation for developing more efficient psychometric procedures to rapidly collect cochlear implant psychophysical data is particularly strong, but any efficient procedures have potential applications in a variety of fields that utilize psychophysical data.

A potential alternative framework to those cited above for developing quick and efficient psychometric methods is the theory of optimal experiments. Federov (1972) and Chernoff (1972) have written extensively about the theory of optimal experiments, and it has been developed fully by many others (MacKay, 1992; Whaite & Ferrie, 1997). The theory of optimal experiments has been extended to a number of fields with applications involving estimating parameters through sequential trials. On the basis of successes in other fields, the theory of optimal experiments could serve as a promising basis for an adaptive psychometric procedure, but it has not been investigated as such to date.

The theory of optimal experiments requires a model of the process under investigation. The parameters of the model can be estimated iteratively on the basis of the outcomes of sequential trials through a two-stage process. First, the parameters for the model are estimated to best fit the observed data. Then a trial is conducted at the stimulus value that will provide the most information about the model, using the most recent estimate of the parameter values.

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