title: Automatic processing of abstract musical tonality.
abstract: Music perception builds on expectancy in harmony, melody, and rhythm. Neural responses to the violations of such expectations are observed in event-related potentials (ERPs) measured using electroencephalography. Most previous ERP studies demonstrating sensitivity to musical violations used stimuli that were temporally regular and musically structured, with less-frequent deviant events that differed from a specific expectation in some feature such as pitch, harmony, or rhythm. Here, we asked whether expectancies about Western musical scale are strong enough to elicit ERP deviance components. Specifically, we explored whether pitches inconsistent with an established scale context elicit deviant components even though equally rare pitches that fit into the established context do not, and even when their timing is unpredictable. We used Markov chains to create temporally irregular pseudo-random sequences of notes chosen from one of two diatonic scales. The Markov pitch-transition probabilities resulted in sequences that favored notes within the scale, but that lacked clear melodic, harmonic, or rhythmic structure. At the random positions, the sequence contained probe tones that were either within the established scale or were out of key. Our subjects ignored the note sequences, watching a self-selected silent movie with subtitles. Compared to the in-key probes, the out-of-key probes elicited a significantly larger P2 ERP component. Results show that random note sequences establish expectations of the "first-order" statistical property of musical key, even in listeners not actively monitoring the sequences.
authors.affiliation: Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA.
authors.forename: Inyong
authors.initials: I
authors.lastname: Choi
authors.affiliation: Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA ; Department of Biomedical Engineering, Boston University Boston, MA, USA.
authors.forename: Hari M
authors.initials: HM
authors.lastname: Bharadwaj
authors.affiliation: Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: Bressler
authors.affiliation: Department of Psychology and Program in Neuroscience and Behavior, Wesleyan University Middletown, CT, USA.
authors.forename: Psyche
authors.initials: P
authors.lastname: Loui
authors.affiliation: Graduate School of Convergence Science and Technology, Seoul National University Suwon, South Korea.
authors.forename: Kyogu
authors.initials: K
authors.lastname: Lee
authors.affiliation: Center for Computational Neuroscience and Neural Technology, Boston University Boston, MA, USA ; Department of Biomedical Engineering, Boston University Boston, MA, USA.
authors.forename: Barbara G
authors.initials: BG
authors.lastname: Shinn-Cunningham
keywords: ERP; P2; markov-chain; music; tonality