Synchronization and temporal processing

Iversen, John R.; Balasubramaniam, Ramesh

doi:10.1016/j.cobeha.2016.02.027

Cited by 56 publications

(57 citation statements)

References 81 publications

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“…Another interesting finding in our study is the presence of significantly greater power for improv over scale in the alpha frequency range in brain regions including the PMC, STG, IPL, and the TPJ ( Figures 2B,C,H, Table 1) involved with perceptual-motor planning and control as well as feedback regulation based on external and internal states and goals. In music processing, these regions have been found to be involved in manipulations of musical structures (Zatorre et al, 2010), and motor-auditory interactions mediated through the parietal cortex has been suggested to be required for musical rhythm perception and production (Iversen and Balasubramaniam, 2016). These functions are thought to be indicative of processes involved with music improvisation.…”

Section: Brain Related Activity Differentiating Improv and Scalementioning

confidence: 99%

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance

Sasaki

Iversen

Callan

2019

Front. Hum. Neurosci.

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This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporalparietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity.

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Section: Brain Related Activity Differentiating Improv and Scalementioning

confidence: 99%

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance

Sasaki

Iversen

Callan

2019

Front. Hum. Neurosci.

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“…The auditory stream may be as simple as a metronome or as complex as a highly layered and time-varying musical work, but the human brain seems to almost automatically seek a simple regularity, the beat, or pulse, which can serve to organize our movements (as in dance, or tapping your foot to music), but also can organize our perception of time (Hannon, Snyder, Eerola, & Krumhansl, 2004;Palmer & Krumhansl, 1990). Two types of timing that are involved in rhythm perception are intervalbased (absolute) timing and beat-based (relative) timing (Dalla Bella et al, 2016;Grube, Lee, Griffiths, Barker, & Woodruff, 2010;Iversen & Balasubramaniam, 2016). Interval-based timing is common to humans and non-human primates (Merchant & Honing, 2014;Zarco, Merchant, Prado, & Mendez, 2009).…”

Section: Beat Perceptionmentioning

confidence: 99%

“…A tight relationship between movement and auditory rhythm perception is evident in human motor system response and motor involvement during music listening and rhythm tasks (Iversen & Balasubramaniam, 2016;Janata, Tomic, & Haberman, 2012;Repp, 2005a;Repp, 2005b;Ross, Warlaumont, Abney, Rigoli, & Balasubramaniam, 2016), and can be observed in neural response to music early in infant development (Kuhl, Ramirez, Bosseler, Lin, & Imada, 2014). How we move to music has by itself become a systematic subfield of inquiry (Ross et al, 2016) that often focuses on body synchronization with music.…”

Section: Introductionmentioning

confidence: 99%

Motor simulation theories of musical beat perception

2016

Self Cite

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There is growing interest in whether the motor system plays an essential role in rhythm perception. The motor system is active during the perception of rhythms, but is such motor activity merely a sign of unexecuted motor planning, or does it play a causal role in shaping the perception of rhythm? We present evidence for a causal role of motor planning and simulation, and review theories of internal simulation for beat-based timing prediction. Brain stimulation studies have the potential to conclusively test if the motor system plays a causal role in beat perception and ground theories to their neural underpinnings. ARTICLE HISTORY

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“…Together, these results demonstrate how fronto-striatal networks play a crucial role in predicting structure in musical rhythm and linguistic syntax (Kotz et al, 2009). Such observations in music have led to the Action Simulation for Auditory Processing hypothesis (Iversen et al, 2009;Iversen and Balasubramaniam, 2016;Patel and Iversen, 2014) proposing that action processing is recruited during predictive coding of music and language.…”

Section: Predictive Codingmentioning

confidence: 70%

Shared neural resources of rhythm and syntax: An ALE Meta-Analysis

Heard

Lee

2019

Preprint

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A growing body of evidence has highlighted behavioral connections between musical rhythm and linguistic syntax, suggesting that these may be mediated by common neural resources. Here, we performed a quantitative meta-analysis of neuroimaging studies using activation likelihood estimate (ALE) to localize the shared neural structures engaged in a representative set of musical rhythm (rhythm, beat, and meter) and linguistic syntax (merge movement, and reanalysis). Rhythm engaged a bilateral sensorimotor network throughout the brain consisting of the inferior frontal gyri, supplementary motor area, superior temporal gyri/temporoparietal junction, insula, the intraparietal lobule, and putamen. By contrast, syntax mostly recruited the left sensorimotor network including the inferior frontal gyrus, posterior superior temporal gyrus, premotor cortex, and supplementary motor area. Intersections between rhythm and syntax maps yielded overlapping regions in the left inferior frontal gyrus, left supplementary motor area, and bilateral insula-neural substrates involved in temporal hierarchy processing and predictive coding. Together, this is the first neuroimaging meta-analysis providing detailed anatomical overlap of sensorimotor regions recruited for musical rhythm and linguistic syntax. Figure 1: Schematics of rhythm and syntax. (A) An example music sequence consisting of quarter and eighth notes. Rhythms (in red) are the pattern of onsets perceived by the listener. Beat and meter (in green and blue) are extracted from the rhythms by the listener. (B) Three representative examples of syntax explored in the present meta-analysis. Merge brings together words or smaller phrases into larger phrases. Movement processes dependent nodes that are often found in whquestions. Reanalysis occurs when extracting complicated grammatical roles resolving ambiguous word orders, such as in the garden path sentence exhibited here. NP: noun phrase; Sent: sentence; Det: determinant; N: noun; Adj: adjective; Wh: question word; VP: verb phrase; Pa: participle.

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Synchronization and temporal processing

Cited by 56 publications

References 81 publications

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance

Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance

Motor simulation theories of musical beat perception

Shared neural resources of rhythm and syntax: An ALE Meta-Analysis

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