Mr Peter Harrison
Email: firstname.lastname@example.orgRoom Number: Peter Landin, CS 416
Research Interests:I work on music-theoretic and cognitive applications of symbolic music modelling. These symbolic music models are based on statistical learning, and derive knowledge of musical structure on the basis of large corpora of music from a variety of genres. I leverage music-theoretic and cognitive research to develop improved techniques for predicting musical structure, and use the resulting models to provide insight into compositional practice and into music cognition.
I also work on modern psychometric approaches to testing musical abilities. My collaborators and I have developed a number of computerised adaptive tests of musical abilities, including the ability to discriminate between melodies, to perceive the beat in a piece of music, and to detect mistuning in vocal music. These tests use adaptive procedures to home in on the test-taker's ability during the test, administering hard items to able candidates and easy items to less able candidates. They also use automatic item generation to produce large banks of items with predetermined psychometric characteristics. The resulting tests have been used in a variety of applications, including an ongoing longitudinal study of musical and academic development through adolescence.
HARRISON PMC (2018). Book review: Statistics and Experimental Design for Psychologists: A model comparison approach by Rory Allen. nameOfConference
HARRISON PMC, Müllensiefen D (2018). Development and Validation of the Computerised Adaptive Beat Alignment Test (CA-BAT). nameOfConference
Harrison PMC (2017). Jordan B. L. Smith, Elaine Chew, & Gérard Assayag (editors), Mathemusical conversations: Mathematics and computation in music performance and composition. nameOfConference
HARRISON PMC (2017). Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. nameOfConference
HARRISON PMC, Musil JJ, Müllensiefen D (2016). Modelling melodic discrimination tests: Descriptive and explanatory approaches. nameOfConference
Müllensiefen D, Harrison P, Caprini F et al. (2015). Investigating the importance of self-theories of intelligence and musicality for students' academic and musical achievement. nameOfConference