Cognitive musicology

Cognitive musicology

Cognitive musicology is a branch of Cognitive Science concerned with computationally modeling musical knowledge with the goal of understanding both music and cognition. [1] More broadly, it can be considered the set of all phenomena surrounding computational modeling of musical thought and action. [2]

Cognitive musicology can be differentiated from the better known field of Music Cognition by a difference in methodological emphasis. Cognitive musicology uses computer modeling to study music-related knowledge representation and has roots in Artificial Intelligence and Cognitive Science. The use of computer models provides an exacting, interactive medium in which to formulate and test theories [3]

This interdisciplinary field investigates topics such as the parallels between language and music in the brain. Biologically inspired models of computation are often included in research, such as neural networks and evolutionary programs. [4] This field seeks to model how musical knowledge is represented, stored, perceived, performed, and generated. By using a well-structured computer environment, the systematic structures of these cognitive phenomena can be investigated. [5]


Notable researchers

The polymath Christopher Longuet-Higgins, who coined the term "cognitive science", is one of the pioneers of cognitive musicology. Among other things, he is noted for the computational implementation of an early key-finding algorithm [6]. Key finding is an essential element of tonal music, and the key-finding problem has attracted considerable attention in the psychology of music over the past several decades. Carol Krumhansl proposed an empirically grounded key-finding algorithm which bears her name [7]. Her approach is based on key-profiles which she painstakingly determined by what has come to be known as the probe-tone technique [8]. David Temperley, whose early work within the field of cognitive musicology applied dynamic programming to aspects of music cognition, has suggested a number of refinements to the Krumhansl Key-Finding Algorithm [9].

Otto Laske was a champion of cognitive musicology [10]. A collection of papers that he co-edited served to highten the visibility of cognitive musicology and to strengthen its association with AI and music [11]. The forward of this book reprints a free-wheeling interview with Marvin Minsky, one of the founding fathers of AI, in which he discusses some of his early writings on music and the mind [12]. AI researcher turned cognitive scientist Douglas Hofstadter has also contributed a number of ideas pertaining to music from an AI perspective [13]. Musician Steve Larson, who worked for a time in Hofstadter's lab, formulated a theory of "musical forces" derived by analogy with physical forces [14]. Hofstadter [15] also weighed in on David Cope's experiments in musical intelligence [16], which take the form of a computer program called EMI which produces music in the form of, say, Bach, or Chopin, or Cope.

Cope's programs are written in Lisp, which turns out to be a popular language for research in cognitive musicology. Desain and Honing have exploited Lisp in their efforts to tap the potential of microworld methodology in cognitive musicology research [17]. Also working in Lisp, Heinrich Taube has explored computer composition from a wide variety of perspectives[18]. There are, of course, researchers who chose to use languages other than Lisp for their research into the computational modeling of musical processes. Tim Rowe, for example, explores "machine musicianship" through C++ programming [19]. A rather different computational methodology for researching musical phenomena is the toolkit approach advocated by David Huron [20]. At a higher level of abstraction, Gerraint Wiggins has investigated general properties of music knowledge representations such as structural generality and expressive completeness [21].

Although a great deal of cognitive musicology research features symbolic computation, notable contributions have been made from the biologically inspired computational paradigms. For example, Jamshed Bharucha and Peter Todd have modeled music perception in tonal music with neural networks [22]. Al Biles has applied genetic algorithms to the composition of jazz solos [23]. Numerous researchers have explored algorithmic composition grounded in a wide range of mathematical formalisms [24][25].

Within cognitive psychology, among the most prominent researchers is Diana Deutsch, who has engaged in a wide variety of work ranging from studies of absolute pitch and musical illusions to the formulation of musical knowledge representations to relationships between music and language [26]. Equally important is Aniruddh Patel, whose work combines traditional methodologies of cognitive psychology with neuroscience. Patel is also the author of a comprehensive survey of cognitive science research on music.[27]

Perhaps the most significant contribution to viewing music from a linguistic perspective is the Generative Theory of Tonal Music (GTTM) proposed by Fred Lerdahl and Ray Jackendoff [28][29]. Although GTTM is presented at the algorithmic level of abstraction rather than the implementational level, their ideas have found computational manifestations in a number of computational projects [30].

Generative science

Cognitive Musicology falls within the realm of the generative sciences. A generative science is an interdisciplinary field of study that explores how the world works through research into specific topics. By studying a given topic from a generative perspective, we can see how it functions with natural laws. By studying cognitive musicology, we can potentially understand how humans think about music and how we can computationally model those thoughts.

Academic programs

Academic programs in Cognitive Musicology and Music Cognition

See also


  1. ^ Laske, Otto (1999). Navigating New Musical Horizons (Contributions to the Study of Music and Dance). Westport: Greenwood Press. ISBN 9780313306327. 
  2. ^ This definition is modeled after the Newell and Simon (1979) definition of computer science, namely that computer science is the set of all phenomena surrounding computers. See Newell, A. & Simon, H. (1976). Computer Science as empirical inquiry: Symbols and search. Communications of the ACM, 19, 113-126.
  3. ^ Laske, O. (1999). Ai and music: A cornerstone of cognitive musicology. In M. Balaban, K. Ebcioglu, & O. Laske (Eds.), Understanding music with ai: Perspectives on music cognition. Cambridge: The MIT Press.
  4. ^ Graci, C. (2009-2010) A brief tour of the learning sciences featuring a cognitive tool for investigating melodic phenomena. Journal of Educational Technology Systems, 38(2), 181-211.
  5. ^ Hamman, M., 1999. "Structure as Performance: Cognitive Musicology and the Objectification of Procedure," in Otto Laske: Navigating New Musical Horizons, ed. J. Tabor. New York: Greenwood Press.
  6. ^ Longuet-Higgins, C. (1987) Mental Processes: Studies in cognitive science. Cambridge, MA, US: The MIT Press.
  7. ^ Krumhansl, Carol (1990). Cognitive Foundations of Musical Pitch. Oxford Oxfordshire: Oxford University Press. ISBN 019505475X. 
  8. ^ Krumhansl, C. and Kessler, E. (1982). Tracing the dynamic changes in perceived tonal organisation in a spatial representation of musical keys. "Psychological Review, 89", 334-368,
  9. ^ Temperley, David (2001). The Cognition of Basic Musical Structures. Cambridge: MIT Press. ISBN 9780262201346. 
  10. ^ Laske, Otto (1999). Otto Laske. Westport: Greenwood Press. ISBN 9780313306327. 
  11. ^ Balaban, Mira (1992). Understanding Music with Ai. Menlo Park: AAAI Press. ISBN 0262521709. 
  12. ^ Minsky, M. (1981). Music, mind, and meaning. Computer Music Journal, 5(3), 28-44. Retrieved December 1, 2009 from
  13. ^ Hofstadter, Douglas (1999). G©œdel, Escher, Bach. New York: Basic Books. ISBN 9780465026562. 
  14. ^ Larson, S. (2004). Musical Forces and Melodic Expectations: Comparing Computer Models with Experimental Results. "Music Perception, 21"(4), 457-498
  15. ^ Cope, David (2004). Virtual Music. Cambridge: The MIT Press. ISBN 9780262532617. 
  16. ^ Cope, David (1996). Experiments in Musical Intelligence. Madison: A-R Editions. ISBN 9780895793379. 
  17. ^ Honing, H. (1993). A microworld approach to formalizing musical knowledge. "Computers and the Humanities, 27"(1), 41-47
  18. ^ Taube, Heinrich (2004). Notes from the Metalevel. New York: Routledge. ISBN 9789026519758. 
  19. ^ Rowe, Robert (2004). Machine Musicianship. City: MIT Pr. ISBN 9780262681490. 
  20. ^ Huron, D. (2002). Music Information Processing Using the Humdrum Toolkit: Concepts, Examples, and Lessons. "Computer Music Journal, 26"(2), 11-26.
  21. ^ Wiggins, G. et. al. (1993). A Framework for the Evaluation of Music Representation Systems. "Computer Music Journal, 17"(3), 31-42.
  22. ^ Bharucha, J. J., & Todd, P. M. (1989). Modeling the perception of tonal structure with neural nets. Computer Music Journal, 44−53
  23. ^ Biles, J. A. 1994. "GenJam: A Genetic Algorithm for Generating Jazz Solos." Proceedings of the 1994 International Computer Music Conference. San Francisco: International Computer Music Association
  24. ^ Nierhaus, Gerhard (2008). Algorithmic Composition. Berlin: Springer. ISBN 9783211755396. 
  25. ^ Cope, David (2005). Computer Models of Musical Creativity. Cambridge: MIT Press. ISBN 9780262033381. 
  26. ^ Deutsch, Diana (1999). The Psychology of Music. Boston: Academic Press. ISBN 9780122135651. 
  27. ^ Patel, Aniruddh (1999). Music, Language, and the Brain. Oxford: Oxford University Press. ISBN 9780122135651. 
  28. ^ Lerdahl, Fred; Ray Jackendoff (1996). A Generative Theory of Tonal Music. Cambridge: MIT Press. ISBN 9780262621076. 
  29. ^ Katz, Jonah; David Pesetsky (2009) "The Identity Thesis for Language and Music"
  30. ^ Masatoshi, H. & Keiji, H. & Satoshi, T. (2006). Implementing A Generative Theory of Tonal Music. "Journal of New Music Research 35"(4), 249-247

Further reading

  • Seifert, Uwe (2010): Investigating the Musical Mind: Situated Cognition, Artistic Human-Robot Interaction Design, and Cognitive Musicology (English/Korean). In: Principles of Media Convergence in the Digital Age. Proceedings of the EWHA HK International Conference 2010, pp. 61–82.
  • Seifert, Uwe (1991): The Schema Concept: A Critical Review of its Development and Current Use in Cognitive Science and Research on Music Perception. In: A. Camurri/C. Canepa (Eds.), Proceedings of the IX CIM Colloquium on Musical Informatics, Genova: AIMI/DIST, pp. 116–131.
  • Aiello, R., & Sloboda, J. (1994). Musical perceptions. Oxford Oxfordshire: Oxford University Press. —A balanced collection of papers by some of the leading figures in the field of music perception and cognition. Opening chapters on emotion and meaning in music (by Leonard B. Meyer) and the Music as Language metaphor (Rita Aiello) are followed by a range of insightful papers on the perception of music by Niclolous Cook, W. Jay Downling, Jamshed Baruscha, and others.
  • Levitin, D. (2007). This is your brain on music. New York: Plume. —Recording engineer turned music psychologist Daniel Levitin talks about the psychology of music in an up tempo, informal, and personal way. Examples drawn from rock and related genres and the limited use of technical terms are two features of the book that make the book appealing to a wide audience.
  • Jourdain, R. (1997). Music, the brain, and ecstasy. New York: Harper Collins. —A far-reaching study of how music captivates us so completely and why we form such powerful connections to it. Leading us to an understanding of the pleasures of sound, Robert Jourdain draws on a variety of fields including science, psychology, and philosophy.

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