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ORCID

https://orcid.org/0000-0001-5283-243X

Date of Award

Spring 5-2020

Author's School

McKelvey School of Engineering

Author's Department

Computer Science & Engineering

Degree Name

Master of Science (MS)

Degree Type

Thesis

Abstract

Psychometric functions model the relationship between a physical phenomenon, an independent variable, and a subject’s performance on a cognitive task. The estimation of these psychometric functions is critical for the understanding of perception and cognition as well as for the diagnosis and treatment of many sensory conditions. The ability to estimate psychometric functions of any complexity is necessary to this end. In the following thesis, a generalized likelihood function for psychometric function estimation with Gaussian processes is described and validated. Such a likelihood function is necessary to enable the usage of Gaussian processes for the estimation of non-zero guess and lapse rate psychometric functions. It is also applicable, in general, to any problem where the probability of one or more classes has theoretical non-whole upper or lower asymptotes.

Language

English (en)

Chair

Dennis Barbour

Committee Members

Netanel Raviv, Brian Garnett

Available for download on Tuesday, October 20, 2020

Included in

Engineering Commons

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