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Permanent link for this collectionhttps://hdl.handle.net/2022/21791

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    Interval patterns are dependent on metrical position in jazz solos
    (Musicae Scientiae, 2021-08-28) Cross, Peter; Goldman, Andrew
    During jazz improvisation, performers employ short recurrent musical motifs called licks. Past research has focused on the pitch, intervallic, and rhythmic characteristics of licks, but less attention has been paid to whether they tend to start in the same place within the measure (metrical dependence). Licks might be metrically dependent, and where a given lick starts in a measure (metrical position) may thus be part of the performer’s mental representation of that lick. Here we report the use of a corpus study to investigate whether licks are metrically dependent. We analyzed a subset of solos, all those in 4/4 time (n = 435), from the Weimar Jazz Database (WJD; Pfleiderer et al., 2017). Using a sliding window technique, we identified melodic sequences (interval n-grams) between 3 and 10 intervals in length. We counted the number of times each interval n-gram occurred, and noted the metrical position of the initial note of each occurrence, using different levels of quantization (8th and 16th note). We compared the entropy of the distribution of metrical positions for each n-gram—with lower values indicating a stronger metrical dependence—against simulated counterparts that assumed no relationship between an n-gram and its metrical position (no metrical dependence). Overall, we found that shorter n-grams were metrically dependent, with varying results for longer n-grams. We suggest two possible explanations: either mental representations of licks may encode their metrical features or the metrical position may make certain licks more accessible to the performer. On the basis of our findings we discuss future studies that could employ our methods.
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    Spatiospectral brain networks reflective of improvisational experience
    (NeuroImage, 2021-11-15) Faller, Josef; Goldman, Andrew; Lin, Yida; McIntosh, James R.; Sajda, Paul
    Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.
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    Reassessing Syntax-Related ERP Components Using Popular Music Chord Sequences: A Model-Based Approach
    (Music Perception, 2021-12-01) Goldman, Andrew
    Electroencephalographic 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.