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Reassessing syntax-related ERP components using popular music chord sequences: A model-based approach

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Goldman, A 
Harrison, PMC 
Jackson, T 
Pearce, MT 

Abstract

jats:pElectroencephalographic responses to unexpected musical events allow researchers to test listeners’ internal models of syntax. One major challenge is dissociating cognitive syntactic violations—based on the abstract identity of a particular musical structure—from unexpected acoustic features. Despite careful controls in past studies, recent work by Bigand, Delbe, Poulin-Carronnat, Leman, and Tillmann (2014) has argued that ERP findings attributed to cognitive surprisal cannot be unequivocally separated from sensory surprisal. Here we report a novel EEG paradigm that uses three auditory short-term memory models and one cognitive model to predict surprisal as indexed by several ERP components (ERAN, N5, P600, and P3a), directly comparing sensory and cognitive contributions. Our paradigm parameterizes a large set of stimuli rather than using categorically “high” and “low” surprisal conditions, addressing issues with past work in which participants may learn where to expect violations and may be biased by local context. The cognitive model (Harrison & Pearce, 2018) predicted higher P3a amplitudes, as did Leman’s (2000) model, indicating both sensory and cognitive contributions to expectation violation. However, no model predicted ERAN, N5, or P600 amplitudes, raising questions about whether traditional interpretations of these ERP components generalize to broader collections of stimuli or rather are limited to less naturalistic stimuli.</jats:p>

Description

Keywords

5202 Biological Psychology, 52 Psychology, Behavioral and Social Science, Neurosciences, Mental health

Journal Title

Music Perception

Conference Name

Journal ISSN

0730-7829
1533-8312

Volume Title

Publisher

University of California Press

Rights

All rights reserved
Sponsorship
PH was supported by a doctoral studentship from the EPSRC and AHRC Centre for Doctoral Training in Media and Arts Technology (EP/L01632X/1).