Psychophysical detection testing with Bayesian active learning

Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham

Research output: Contribution to conferencePaperpeer-review

17 Scopus citations


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.

Original languageEnglish
Number of pages10
StatePublished - 2015
Event31st Conference on Uncertainty in Artificial Intelligence, UAI 2015 - Amsterdam, Netherlands
Duration: Jul 12 2015Jul 16 2015


Conference31st Conference on Uncertainty in Artificial Intelligence, UAI 2015


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