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Improvisation and the self-organization of multiple musical bodies.

Walton AE, Richardson MJ, Langland-Hassan P, Chemero A - Front Psychol (2015)

Bottom Line: Investigations of this behavior have traditionally focused on describing the organization of cognitive structures.The focus, here, however, is on the ability of the time-evolving patterns of inter-musician movement coordination as revealed by the mathematical tools of complex dynamical systems to provide a new understanding of what potentiates the novelty of spontaneous musical action.Revealing the sophistication of the previously unexplored dynamics of movement coordination between improvising musicians is an important step toward understanding how creative musical expressions emerge from the spontaneous coordination of multiple musical bodies.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Center for Cognition, Action and Perception, University of Cincinnati Cincinnati, OH, USA.

ABSTRACT
Understanding everyday behavior relies heavily upon understanding our ability to improvise, how we are able to continuously anticipate and adapt in order to coordinate with our environment and others. Here we consider the ability of musicians to improvise, where they must spontaneously coordinate their actions with co-performers in order to produce novel musical expressions. Investigations of this behavior have traditionally focused on describing the organization of cognitive structures. The focus, here, however, is on the ability of the time-evolving patterns of inter-musician movement coordination as revealed by the mathematical tools of complex dynamical systems to provide a new understanding of what potentiates the novelty of spontaneous musical action. We demonstrate this approach through the application of cross wavelet spectral analysis, which isolates the strength and patterning of the behavioral coordination that occurs between improvising musicians across a range of nested time-scales. Revealing the sophistication of the previously unexplored dynamics of movement coordination between improvising musicians is an important step toward understanding how creative musical expressions emerge from the spontaneous coordination of multiple musical bodies.

No MeSH data available.


Related in: MedlinePlus

Cross wavelet plots of the lateral movements of the musicians’ right forearms, displaying the strength of coherence at each period (red for high coherence = 1, dark blue for low to no coherence = 0), as well as relative phase angle (right arrows equal in-phase coordination, left arrows equal anti-phase coordination). (A) Displays the coordination between two piano players playing the exact same part, in synchrony with the ostinato backing track. (B) Displays coordination while the musicians improvise over the ostinato backing track.
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Figure 1: Cross wavelet plots of the lateral movements of the musicians’ right forearms, displaying the strength of coherence at each period (red for high coherence = 1, dark blue for low to no coherence = 0), as well as relative phase angle (right arrows equal in-phase coordination, left arrows equal anti-phase coordination). (A) Displays the coordination between two piano players playing the exact same part, in synchrony with the ostinato backing track. (B) Displays coordination while the musicians improvise over the ostinato backing track.

Mentions: More specifically, cross wavelet analysis assesses coordination between two time series through spectral decomposition, and subsequent examination of the strength (coherence) and patterning (relative phase) of the coordination that occurs between participants across multiple time scales (see Grinsted et al., 2004; Issartel et al., 2014, for a more detailed introduction). The strength of coordination and the relative phase angle between two time series is assessed for shorter, half second and second-to-second time-scales, as well as at longer 4, 8, 12, and 16 s time-scales. Figures 1 and 2 demonstrate the use of cross wavelet analysis to investigate the coordination between the lateral movements of the forearms of two piano players. Their movements were recorded using wireless motion-tracking sensors attached to their wrists while they improvised over a musical backing track. The time series of their limb movements were then analyzed using functions available in MathWorks’ free wavelet toolbox [Copyright (C) 2002-2004, Aslak Grinsted]. The movement time-series were also low-pass filtered prior to analysis using a 10 Hz Butterworth filter. For each of the different time scales an average measure of the correlation of the time series was calculated on a scale from 0 to 1, as well as the average distribution of relative phase angles (DRP) that occurred between the musicians’ movement time series. One can observe the strength of coherence over the course of the performance as denoted by color (red for high coherence, dark blue for low to no coherence) for each period (in units of seconds) on the y-axis. The arrows correspond to the relative phase of the coordination. Right arrows equal in-phase coordination (the two systems are visiting the same states in perfect synchrony) and left arrows equal anti-phase coordination (the two system are visiting states that are in perfect opposition).


Improvisation and the self-organization of multiple musical bodies.

Walton AE, Richardson MJ, Langland-Hassan P, Chemero A - Front Psychol (2015)

Cross wavelet plots of the lateral movements of the musicians’ right forearms, displaying the strength of coherence at each period (red for high coherence = 1, dark blue for low to no coherence = 0), as well as relative phase angle (right arrows equal in-phase coordination, left arrows equal anti-phase coordination). (A) Displays the coordination between two piano players playing the exact same part, in synchrony with the ostinato backing track. (B) Displays coordination while the musicians improvise over the ostinato backing track.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4403292&req=5

Figure 1: Cross wavelet plots of the lateral movements of the musicians’ right forearms, displaying the strength of coherence at each period (red for high coherence = 1, dark blue for low to no coherence = 0), as well as relative phase angle (right arrows equal in-phase coordination, left arrows equal anti-phase coordination). (A) Displays the coordination between two piano players playing the exact same part, in synchrony with the ostinato backing track. (B) Displays coordination while the musicians improvise over the ostinato backing track.
Mentions: More specifically, cross wavelet analysis assesses coordination between two time series through spectral decomposition, and subsequent examination of the strength (coherence) and patterning (relative phase) of the coordination that occurs between participants across multiple time scales (see Grinsted et al., 2004; Issartel et al., 2014, for a more detailed introduction). The strength of coordination and the relative phase angle between two time series is assessed for shorter, half second and second-to-second time-scales, as well as at longer 4, 8, 12, and 16 s time-scales. Figures 1 and 2 demonstrate the use of cross wavelet analysis to investigate the coordination between the lateral movements of the forearms of two piano players. Their movements were recorded using wireless motion-tracking sensors attached to their wrists while they improvised over a musical backing track. The time series of their limb movements were then analyzed using functions available in MathWorks’ free wavelet toolbox [Copyright (C) 2002-2004, Aslak Grinsted]. The movement time-series were also low-pass filtered prior to analysis using a 10 Hz Butterworth filter. For each of the different time scales an average measure of the correlation of the time series was calculated on a scale from 0 to 1, as well as the average distribution of relative phase angles (DRP) that occurred between the musicians’ movement time series. One can observe the strength of coherence over the course of the performance as denoted by color (red for high coherence, dark blue for low to no coherence) for each period (in units of seconds) on the y-axis. The arrows correspond to the relative phase of the coordination. Right arrows equal in-phase coordination (the two systems are visiting the same states in perfect synchrony) and left arrows equal anti-phase coordination (the two system are visiting states that are in perfect opposition).

Bottom Line: Investigations of this behavior have traditionally focused on describing the organization of cognitive structures.The focus, here, however, is on the ability of the time-evolving patterns of inter-musician movement coordination as revealed by the mathematical tools of complex dynamical systems to provide a new understanding of what potentiates the novelty of spontaneous musical action.Revealing the sophistication of the previously unexplored dynamics of movement coordination between improvising musicians is an important step toward understanding how creative musical expressions emerge from the spontaneous coordination of multiple musical bodies.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Center for Cognition, Action and Perception, University of Cincinnati Cincinnati, OH, USA.

ABSTRACT
Understanding everyday behavior relies heavily upon understanding our ability to improvise, how we are able to continuously anticipate and adapt in order to coordinate with our environment and others. Here we consider the ability of musicians to improvise, where they must spontaneously coordinate their actions with co-performers in order to produce novel musical expressions. Investigations of this behavior have traditionally focused on describing the organization of cognitive structures. The focus, here, however, is on the ability of the time-evolving patterns of inter-musician movement coordination as revealed by the mathematical tools of complex dynamical systems to provide a new understanding of what potentiates the novelty of spontaneous musical action. We demonstrate this approach through the application of cross wavelet spectral analysis, which isolates the strength and patterning of the behavioral coordination that occurs between improvising musicians across a range of nested time-scales. Revealing the sophistication of the previously unexplored dynamics of movement coordination between improvising musicians is an important step toward understanding how creative musical expressions emerge from the spontaneous coordination of multiple musical bodies.

No MeSH data available.


Related in: MedlinePlus