EEG oscillatory patterns are associated with error prediction during music performance and are altered in musician's dystonia

Ruiz, María Herrojo; Strübing, Felix; Jabusch, Hans‐Christian; Altenmüller, Eckart

doi:10.1016/j.neuroimage.2010.12.050

Cited by 64 publications

(63 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may therefore reflect an increased signaling of need for control by the pMFC which is instantaneously implemented by other, more executive regions leading to benefits in performance (Cavanagh & Frank, 2014). However, it may also reflect the implementation of control in the pMFC itself (e.g., Aarts, Roelofs, & Turennout, 2008;Roelofs, van Turennout, & Coles, 2006;Ruiz, Strübing, Jabusch, & Altenmüller, 2011). Finally, we observed higher FMθ power for precise compared to more imprecise performance in a later time window, 400 to 600 ms following the response.…”

Section: Task Performance Stagementioning

confidence: 70%

Face-induced expectancies influence neural mechanisms of performance monitoring

Osinsky

Seeger

Mussel

et al. 2015

Cogn Affect Behav Neurosci

View full text Add to dashboard Cite

In many daily situations, the consequences of our actions are predicted by cues that are often social in nature. For instance, seeing the face of an evaluator (e.g., a supervisor at work) may activate certain evaluative expectancies, depending on the history of prior encounters with that particular person. We investigated how such face-induced expectancies influence neurocognitive functions of performance monitoring. We recorded an electroencephalogram while participants completed a time-estimation task, during which they received performance feedback from a strict and a lenient evaluator. During each trial, participants first saw the evaluator's face before performing the task and, finally, receiving feedback. Therefore, faces could be used as predictive cues for the upcoming evaluation. We analyzed electrocortical signatures of performance monitoring at the stages of cue processing, task performance, and feedback reception. Our results indicate that, at the cue stage, seeing the strict evaluator's face results in an anticipatory preparation of fronto-medial monitoring mechanisms, as reflected by a sustained negative-going amplitude shift (i.e., the contingent negative variation). At the performance stage, face-induced expectancies of a strict evaluation rule led to increases of early performance monitoring signals (i.e., frontal-midline theta power). At the final stage of feedback reception, violations of outcome expectancies differentially affected the feedback-related negativity and frontalmidline theta power, pointing to a functional dissociation between these signatures. Altogether, our results indicate that evaluative expectancies induced by face-cues lead to adjustments of internal performance monitoring mechanisms at various stages of task processing.

show abstract

Section: Task Performance Stagementioning

confidence: 70%

Face-induced expectancies influence neural mechanisms of performance monitoring

Osinsky

Seeger

Mussel

et al. 2015

Cogn Affect Behav Neurosci

View full text Add to dashboard Cite

show abstract

“…The y-axis does not have the measure unit as spectral coherence in dimensionless. error prediction during music performance and have been found altered in musician's dystonia (Ruiz et al, 2011).…”

Section: Discussionmentioning

confidence: 98%

Movement-induced uncoupling of primary sensory and motor areas in focal task-specific hand dystonia

et al. 2013

View full text Add to dashboard Cite

“…The neurophysical mechanisms of timing in the millisecond range are still widely open (30,31). EEG oscillatory patterns are associated with error prediction during music performance (32). Fine motor skills, such as finger-tapping rhythm and rate, are used to establish an early diagnosis of Huntington disease (33).…”

Section: Significancementioning

confidence: 99%

Synchronization in human musical rhythms and mutually interacting complex systems

Hennig

2014

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Though the music produced by an ensemble is influenced by multiple factors, including musical genre, musician skill, and individual interpretation, rhythmic synchronization is at the foundation of musical interaction. Here, we study the statistical nature of the mutual interaction between two humans synchronizing rhythms. We find that the interbeat intervals of both laypeople and professional musicians exhibit scale-free (power law) cross-correlations. Surprisingly, the next beat to be played by one person is dependent on the entire history of the other person's interbeat intervals on timescales up to several minutes. To understand this finding, we propose a general stochastic model for mutually interacting complex systems, which suggests a physiologically motivated explanation for the occurrence of scale-free cross-correlations. We show that the observed long-term memory phenomenon in rhythmic synchronization can be imitated by fractal coupling of separately recorded or synthesized audio tracks and thus applied in electronic music. Though this study provides an understanding of fundamental characteristics of timing and synchronization at the interbrain level, the mutually interacting complex systems model may also be applied to study the dynamics of other complex systems where scale-free crosscorrelations have been observed, including econophysics, physiological time series, and collective behavior of animal flocks.time series analysis | long-range cross-correlations | anticorrelations | musical coupling | interbrain synchronization I n his book Musicophilia, neurologist Oliver Sacks writes: "In all societies, a primary function of music is collective and communal, to bring and bind people together. People sing together and dance together in every culture, and one can imagine them having done so around the first fires, a hundred thousand years ago" (1). Sacks adds, "In such a situation, there seems to be an actual binding of nervous systems accomplished by rhythm" (2). These thoughts lead to the question: Is there any underlying and quantifiable structure to the subjective experience of "musical binding"? Here, we examine the statistical nature of musical binding (also referred to as musical coupling) when two humans play rhythms in synchrony.Every beat a single (noninteracting) layperson or musician plays is accompanied by small temporal deviations from the exact beat pattern, i.e., even a trained musician will hit a drum beat slightly ahead or behind the metronome (with a SD of typically 5-15 ms). Interestingly, these deviations are statistically dependent and exhibit long-range correlations (LRC) (3, 4). Listeners significantly prefer music mirroring long-range correlated temporal deviations over uncorrelated (white noise) fluctuations (5, 6). LRC are also inherent in the reproduction of both spatial and temporal intervals of single subjects (4, 7-9) and in musical compositions, such as pitch fluctuations (a simple example of pitch fluctuations is a melody) (10, 11) and note lengths (12). The observation of ...

show abstract

EEG oscillatory patterns are associated with error prediction during music performance and are altered in musician's dystonia

Cited by 64 publications

References 83 publications

Face-induced expectancies influence neural mechanisms of performance monitoring

Face-induced expectancies influence neural mechanisms of performance monitoring

Movement-induced uncoupling of primary sensory and motor areas in focal task-specific hand dystonia

Synchronization in human musical rhythms and mutually interacting complex systems

Contact Info

Product

Resources

About