QUEST [Watson and Pelli, Perception and Psychophysics, 13, 113-120 (1983)] is an efficient
method of measuring thresholds which is based on three steps: (1) Specification of
prior knowledge and assumptions, including an initial probability density function
(p.d.f.) of threshold (i.e. relative probability of different thresholds in the population).
(2) A method for choosing the stimulus intensity of any trial. (3) A method for choosing
the final threshold estimate. QUEST introduced a Bayesian framework for combining
prior knowledge with the results of previous trials to calculate a current p.d.f.;
this is then used to implement Steps 2 and 3. While maintaining this Bayesian approach,
this paper evaluates whether modifications of the QUEST method (particularly Step
2, but also Steps 1 and 3) can lead to greater precision and reduced bias. Four variations
of the QUEST method (differing in Step 2) were evaluated by computer simulations.
In addition to the standard method of setting the stimulus intensity to the mode of
the current p.d.f. of threshold, the alternatives of using the mean and the median
were evaluated. In the fourth variation--the Minimum Variance Method--the next stimulus
intensity is chosen to minimize the expected variance at the end of the next trial.
An exact enumeration technique with up to 20 trials was used for both yes-no and two-alternative
forced-choice (2AFC) experiments. In all cases, using the mean (here called ZEST)
provided better precision than using the median which in turn was better than using
the mode. The Minimum Variance Method provided slightly better precision than ZEST.
The usual threshold criterion--based on the "ideal sweat factor"--may not provide
optimum precision; efficiency can generally be improved by optimizing the threshold
criterion. We therefore recommend either using ZEST with the optimum threshold criterion
or the more complex Minimum Variance Method. A distinction is made between "measurement
bias", which is derived from the mean of repeated threshold estimates for a single
real threshold, and "interpretation bias", which is derived from the mean of real
thresholds yielding a single threshold estimate. If their assumptions are correct,
the current methods have no interpretation bias, but they do have measurement bias.
Interpretation bias caused by errors in the assumptions used by ZEST is evaluated.
The precisions and merits of yes-no and 2AFC techniques are compared.(ABSTRACT TRUNCATED
AT 400 WORDS)