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Cognitive musicology is a branch of cognitive science concerned with computationally modeling musical knowledge with the goal of understanding both music and cognition. Cognitive musicology can be differentiated from other branches of music psychology via its methodological emphasis, using computer modeling to study music-related knowledge representation with roots in artificial intelligence and cognitive science. The use of computer models provides an exacting, interactive medium in which to formulate and test theories.〔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.〕 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.〔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.〕 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.〔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.〕 ==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.〔Longuet-Higgins, C. (1987) Mental Processes: Studies in cognitive science. Cambridge, MA, US: The MIT Press.〕 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 and Mark Schmuckler proposed an empirically grounded key-finding algorithm which bears their names. Their approach is based on key-profiles which were painstakingly determined by what has come to be known as the probe-tone technique.〔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,〕 This algorithm has successfully been able to model the perception of musical key in short excerpts of music, as well as to track listeners' changing sense of key movement throughout an entire piece of music.〔Schmuckler, M. A., & Tomovski, R.(2005) Perceptual tests of musical key-finding. ''Journal of Experimental Psychology: Human Perception and Performance, 31'', 1124–1149,〕 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-Schmuckler Key-Finding Algorithm. Otto Laske was a champion of cognitive musicology. A collection of papers that he co-edited served to heighten the visibility of cognitive musicology and to strengthen its association with AI and music. The foreword 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.〔Minsky, M. (1981). Music, mind, and meaning. ''Computer Music Journal, 5''(3), 28-44. Retrieved December 1, 2009 from http://web.media.mit.edu/~minsky/papers/MusicMindMeaning.html〕 AI researcher turned cognitive scientist Douglas Hofstadter has also contributed a number of ideas pertaining to music from an AI perspective. Musician Steve Larson, who worked for a time in Hofstadter's lab, formulated a theory of "musical forces" derived by analogy with physical forces.〔Larson, S. (2004). Musical Forces and Melodic Expectations: Comparing Computer Models with Experimental Results. "Music Perception, 21" (4), 457–498〕 Hofstadter also weighed in on David Cope's experiments in musical intelligence, 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.〔Honing, H. (1993). A microworld approach to formalizing musical knowledge. "Computers and the Humanities, 27" (1), 41–47〕 Also working in Lisp, Heinrich Taube has explored computer composition from a wide variety of perspectives. 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. A rather different computational methodology for researching musical phenomena is the toolkit approach advocated by David Huron.〔Huron, D. (2002). Music Information Processing Using the Humdrum Toolkit: Concepts, Examples, and Lessons. "Computer Music Journal, 26" (2), 11–26.〕 At a higher level of abstraction, Gerraint Wiggins has investigated general properties of music knowledge representations such as structural generality and expressive completeness.〔Wiggins, G. et al. (1993). A Framework for the Evaluation of Music Representation Systems. "Computer Music Journal, 17" (3), 31–42.〕 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.〔Bharucha, J. J., & Todd, P. M. (1989). Modeling the perception of tonal structure with neural nets. Computer Music Journal, 44−53〕 Al Biles has applied genetic algorithms to the composition of jazz solos.〔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〕 Numerous researchers have explored algorithmic composition grounded in a wide range of mathematical formalisms. 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. Equally important is Aniruddh D. 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. 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. 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. For the German-speaking area, Laske's conception of cognitive musicology has been advanced by Uwe Seifert in his book ''Systematische Musiktheorie und Kognitionswissenschaft. Zur Grundlegung der kognitiven Musikwissenschaft'' ("Systematic music theory and cognitive science. The foundation of cognitive musicology")〔Uwe Seifert: ''Systematische Musiktheorie und Kognitionswissenschaft. Zur Grundlegung der kognitiven Musikwissenschaft''. Orpheus Verlag für systematische Musikwissenschaft, Bonn 1993〕 and subsequent publications. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「cognitive musicology」の詳細全文を読む スポンサード リンク
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