Acquiring a New Musical System
by
Psyche Loui
B.S. (Duke University) 2003

A dissertation submitted in partial satisfaction of the
requirements for the degree of
Doctor of Philosophy
in
Psychology
in the

Graduate Division
of the
University of California, Berkeley

Committee in charge:

Professor David L. Wessel, Chair
Professor Ervin R. Hafter
Professor Carla L. Hudson Kam
Professor Edmund Campion

Spring 2007

Abstract

A

fundamental mystery in music cognition concerns whether and how the human brain

can develop expectations and preferences for events in the auditory

environment. My thesis uses behavioral and electrophysiological methods to

investigate the learning of a novel system of musical sounds. We design a new

musical system based on the Bohlen-Pierce scale, a microtonal scale tuned

differently from the traditional Western musical scale. Chord progressions and

melodies were composed in this scale as legal exemplars of two finite-state

grammars. In a series of behavioral studies, participants were presented with

melodies in one of the two grammars, followed by several tests assessing

grammar-learning, sensitivity to frequency of occurrence, and preference for

melodies. Results demonstrate that given exposure to a small number of

melodies, listeners recognized and preferred melodies they had heard, but when

exposed to a sufficiently large set of melodies, listeners were able to learn

the underlying statistical regularities of their given grammar. These effects

were influenced by pshychoacoustic and statistical properties of the exposure,

and were replicable with transposed melodies and for scales with different

harmonies. Electrophysiological recordings (Event-Related Potentials) in

response to chords in the new musical system revealed two components of

cortical activity which are sensitive to the probability of occurrence and the

amount of exposure of sounds in the musical context. We conclude that the human

brain can rapidly acquire various structural and statistical aspects of sounds,

and that neural mechanisms subserving statistical learning may be vital to

music as well as other cognitive and perceptual functions more generally.

Dedication

To

Mom and Dad

Acknowledgements

This

dissertation would not have been possible without the guidance and support of

various individuals from within and beyond UC Berkeley. I would like to thank David Wessel, my advisor and dissertation chair, for his unfailing support, expertise, and

advice along the way. I would like to thank my advisor Erv Hafter for his

kindness and support, and for his lessons in science and life. I express

sincere gratitude to Bob Knight for his expertise in neuroscience as well as

his kind advice and trust. To Carla Hudson Kam and the Language and Learning

Lab (especially Amy Finn, Whitney Goodrich, and Tim Beyer) I am most indebted

for advice, stimulating conversations, and moral support. For helpful

discussions and technical support I thank Edmund Campion and the Center for New

Music and Audio Technologies (especially Michael Zbyzynski, John MacCallum, Aaron Einbond, Brian Vogel, and Peter Kassakian), the Auditory Perception lab

(Anne-Marie “Nannick” Bonnel, Tassos Sarampalis, Bernhard Seeber,

and Andy Schmeder) and the Knight lab (Mark Kishiyama, Christina Karns, Cathrine Dam). I am extremely grateful to Tom Wickens for his generosity with help on

statistical methods and especially with my use of his lab space. I owe a big

thank you to all my research assistants over the past few years for their great

productivity, intelligence, and good cheer: Elaine Wu, Pearl Chen, Tiffany Day, Judy Wang, Joann Chang, Young Lee, Jorge Duque, Johannes Sommer, Shaochen Wu, and

Charles Li. I also thank Chris Lucas for help on implementing finite-state

grammars, the Robertson lab (Ani Flevaris, Ayelet Landau, Joe Brooks) for

helpful discussions on ERP methods, and Bill Prinzmetal for his kindness and

support. I thank Carol Krumhansl at Cornell University for helpful advice on

the design of the artificial musical system, and Marty Woldorff at Duke University for advising my undergraduate thesis, which was a vital precursor to the

present dissertation. I thank the UC Berkeley Psychology Department, the

Academic Senate, and NINDS for financial support. Finally, I would like to

thank Wai-Po and Rebecca Loui.