Name: Jonathan Schuett

Psychology Ph.D. Dissertation Defense Presentation

Date: Monday, April 29, 2019

Time: 10:00 AM

Location: JS Coon 150

Advisor:

Professor Bruce Walker, Ph.D. (Georgia Tech)

Thesis Committee Members:

Professor Richard Catrambone, Ph.D. (Georgia Tech)

Professor Frank Durso, Ph.D. (Georgia Tech)

Associate Professor Rick Thomas, Ph.D. (Georgia Tech)

Assistant Professor Michael Nees, Ph.D. (Lafayette)

Title: Measuring the effects of display design and individual differences on the utilization of multi-stream sonifications

Abstract: Previous work in the auditory display community has discussed the impact of both display design and individual listener differences on how successfully listeners can use a sonification. This dissertation extends past findings and explores the effects of display and individual differences on listeners’ ability to utilize a sonification for an analytical listening task when multiple variables are presented simultaneously. This is considered a more complicated task and pushes listeners’ perceptual abilities, but is necessary when wanting to use sonifications to display more detailed information about a dataset. The study used a two by two between-subjects approach to measure the effects of display design and domain mapping. Acoustic parameters were assigned to either the weather or the health domain, and these mappings were either created by an expert sound designer or arbitrarily assigned. The acoustic parameters were originally selected for the weather domain, so those display conditions were expected to result in higher listener accuracy. Results showed that the expert mapped weather sonification led to higher mean listener accuracy than the arbitrarily mapped health display when listeners did not have time to practice, however with less than an hour of practice the significant main effects of design and domain mapping went away and mean accuracy scores increased to a similar level. This dissertation introduces two models for predicting listener accuracy scores, the first model uses musical sophistication and self-reported motivation scores to predict listener accuracy on the task before practice. The second model uses musical sophistication, self-reported motivation, and listening discrimination scores to predict listener accuracy on the sonification task after practice.