Psychophysical detection tests are ubiquitous in
the study of human sensation and the diagnosis
and treatment of virtually all sensory impairments.
In many of these settings, the goal
is to recover, from a series of binary observations
from a human subject, the latent function
that describes the discriminability of a sensory
stimulus over some relevant domain. The auditory
detection test, for example, seeks to understand
a subject's likelihood of hearing sounds as
a function of frequency and amplitude. Conventional
methods for performing these tests involve
testing stimuli on a pre-determined grid. This
approach not only samples at very uninformative
locations, but also fails to learn critical features
of a subject's latent discriminability function.
Here we advance active learning with Gaussian
processes to the setting of psychophysical
testing. We develop a model that incorporates
strong prior knowledge about the class of stimuli,
we derive a sensible method for choosing sample
points, and we demonstrate how to evaluate this
model efficiently. Finally, we develop a novel
likelihood that enables testing of multiple stimuli
simultaneously. We evaluate our method in
both simulated and real auditory detection tests,
demonstrating the merit of our approach.
We have also published a clinical trial using these techniques:
Fast, Continuous Audiogram Estimation Using Machine Learning. Xinyu D Song, Brittany M Wallace, Jacob R Gardner, Noah M Ledbetter, Kilian Q Weinberger, Dennis L Barbour. Ear and Hearing 2015.