Improvisation experience predicts how musicians categorize musical structures

Western music improvisers learn to realize chord symbols in multiple ways according to functional classifications, and practice making substitutions of these realizations accordingly. In contrast, Western classical musicians read music that specifies particular realizations so that they rarely make such functional substitutions. We advance a theory that experienced improvisers more readily perceive musical structures with similar functions as sounding similar by virtue of this categorization, and that this categorization partly enables the ability to improvise by allowing performers to make substitutions. We tested this with an oddball task while recording electroencephalography. In the task, a repeating standard chord progression was randomly interspersed with two kinds of deviants: one in which one of the chords was substituted with a chord from the same functional class (“exemplar deviant”), and one in which the substitution was outside the functional class (“function deviant”). For function compared to exemplar deviants, participants with more improvisation experience responded more quickly and accurately and had more discriminable N2c and P3b ERP components. Further, N2c and P3b signal discriminability predicted participants’ behavioral ability to discriminate the stimuli. Our research contributes to the cognitive science of creativity through identifying differences in knowledge organization as a trait that facilitates creative ability.

[1]  S. Pollmann,et al.  Retinotopic Activation in Response to Subjective Contours in Primary Visual Cortex , 2008, Frontiers in human neuroscience.

[2]  J. Sloboda Generative Processes in Music: The Psychology of Performance, Improvisation, and Composition , 1985 .

[3]  Stefan Caris Love,et al.  An Ecological Description of Jazz Improvisation , 2017 .

[4]  L. Nooshin Improvisation as ‘Other’: Creativity, Knowledge and Power – The Case of Iranian Classical Music , 2003, Journal of the Royal Musical Association.

[5]  Garry L. Hagberg,et al.  The Imaginary Museum Of Musical Works , 1992 .

[6]  M. Tervaniemi,et al.  The sound of music: Differentiating musicians using a fast, musical multi-feature mismatch negativity paradigm , 2012, Neuropsychologia.

[7]  P. Berliner Thinking in Jazz: The Infinite Art of Improvisation , 1995 .

[8]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[9]  Martin Norgaard,et al.  Testing cognitive theories by creating a pattern-based probabilistic algorithm for melody and rhythm in jazz improvisation. , 2013 .

[10]  Klaus Frieler,et al.  The Jazzomat project. Issues and methods for the automatic analysis of jazz improvisations , 2009 .

[11]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[12]  H G Vaughan,et al.  Event-related potential correlates of two stages of information processing in physical and semantic discrimination tasks. , 1983, Psychophysiology.

[13]  M. Schlesewsky,et al.  Two routes to actorhood: lexicalized potency to act and identification of the actor role , 2015, Front. Psychol..

[14]  Jeff Pressing,et al.  Improvisation: Methods and models. , 1988 .

[15]  Summer K. Rankin,et al.  Neural Substrates of Interactive Musical Improvisation: An fMRI Study of ‘Trading Fours’ in Jazz , 2014, PloS one.

[16]  Philip N. Johnson-Laird,et al.  How Jazz Musicians Improvise , 2002 .

[17]  David Sudnow Ways of the hand : the organization of improvised conduct , 1978 .

[18]  M. Tervaniemi,et al.  Auditory Profiles of Classical, Jazz, and Rock Musicians: Genre-Specific Sensitivity to Musical Sound Features , 2016, Front. Psychol..

[19]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[20]  I. Winkler,et al.  Top-down effects can modify the initially stimulus-driven auditory organization. , 2002, Brain research. Cognitive brain research.

[21]  B. M. Jong,et al.  Differential parietal and temporal contributions to music perception in improvising and score-dependent musicians, an fMRI study , 2015, Brain Research.

[22]  R. Kakigi,et al.  Musical Training Enhances Automatic Encoding of Melodic Contour and Interval Structure , 2004, Journal of Cognitive Neuroscience.

[23]  W. Hargreaves Generating ideas in jazz improvisation: Where theory meets practice , 2012 .

[24]  Bruno Nettl,et al.  In the course of performance : studies in the world of musical improvisation , 1999 .

[25]  E Donchin,et al.  P300 and stimulus categorization: two plus one is not so different from one plus one. , 1980, Psychophysiology.

[26]  Aaron L. Berkowitz,et al.  Expertise-related deactivation of the right temporoparietal junction during musical improvisation , 2010, NeuroImage.

[27]  Andrew Goldman Towards a Cognitive-Scientific Research Program for Improvisation: Theory and an Experiment , 2013 .

[28]  Fredrik Ullén,et al.  Goal-independent mechanisms for free response generation: Creative and pseudo-random performance share neural substrates , 2012, NeuroImage.

[29]  Martin Norgaard,et al.  How Jazz Musicians Improvise: The Central Role of Auditory and Motor Patterns , 2014 .

[30]  George E. Lewis,et al.  The Oxford handbook of critical improvisation studies , 2016 .

[31]  Andrew Goldman Improvisation as a Way of Knowing , 2016 .

[32]  Peter Vuust,et al.  To musicians, the message is in the meter: Pre-attentive neuronal responses to incongruent rhythm are left-lateralized in musicians , 2005, NeuroImage.

[33]  Katie Zhukov,et al.  Challenging Approaches to Assessment of Instrumental Learning , 2015 .

[34]  Lucas C. Parra,et al.  Recipes for the linear analysis of EEG , 2005, NeuroImage.

[35]  Martin Norgaard,et al.  Descriptions of Improvisational Thinking by Artist-Level Jazz Musicians , 2011 .

[36]  R. Jung,et al.  Neuroimaging creativity: A psychometric view , 2010, Behavioural Brain Research.

[37]  Peter Fransson,et al.  Connecting to Create: Expertise in Musical Improvisation Is Associated with Increased Functional Connectivity between Premotor and Prefrontal Areas , 2014, The Journal of Neuroscience.

[38]  James T. Townsend,et al.  The Stochastic Modeling of Elementary Psychological Processes , 1983 .

[39]  P. Johnson-Laird Jazz Improvization: A Theory at the Computational Level , 1991 .

[40]  Joseph P. Walton,et al.  Effects of Musical Training and Absolute Pitch on the Neural Processing of Melodic Intervals: A P3 Event-Related Potential Study , 1992 .

[41]  R. C. Oldfield THE ASSESSMENT AND ANALYSIS OF HANDEDNESS , 1971 .

[42]  M. Tervaniemi,et al.  Representation of abstract attributes of auditory stimuli in the human brain. , 1992, Neuroreport.

[43]  Fredrik Ullén,et al.  Cortical Regions Involved in the Generation of Musical Structures during Improvisation in Pianists , 2007, Journal of Cognitive Neuroscience.

[44]  J. Polich Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[45]  A. Braun,et al.  Neural Correlates of Lyrical Improvisation: An fMRI Study of Freestyle Rap , 2012, Scientific Reports.

[46]  Scott T. Grafton,et al.  Goal Representation in Human Anterior Intraparietal Sulcus , 2006, The Journal of Neuroscience.

[47]  James T. Townsend,et al.  Methods of Modeling Capacity in Simple Processing Systems , 2014 .

[48]  Peter E. Keller,et al.  A conceptual review on action-perception coupling in the musicians’ brain: what is it good for? , 2014, Front. Hum. Neurosci..

[49]  A. Ishai,et al.  Recollection- and Familiarity-Based Decisions Reflect Memory Strength , 2008, Frontiers in systems neuroscience.

[50]  Aaron L. Berkowitz,et al.  Generation of novel motor sequences: The neural correlates of musical improvisation , 2008, NeuroImage.

[51]  A. Braun,et al.  Neural Substrates of Spontaneous Musical Performance: An fMRI Study of Jazz Improvisation , 2008, PloS one.

[52]  Jakob Abeßer,et al.  Midlevel analysis of monophonic jazz solos: A new approach to the study of improvisation , 2016 .