Computational modeling of music cognition: A case study on model selection

Music Perception

Volumn 23, Issue 5, 2006, Pages 365-376

  Honing, Henkjan ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33744455757     PISSN: 07307829     EISSN: None     Source Type: Journal    
DOI: 10.1525/mp.2006.23.5.365     Document Type: Article

References (42)

1. Clarke, E. F. (1999). Rhythm and timing in music. In D. Deutsch (Ed.), Psychology of music (2nd ed., pp. 473-500). New York: Academic Press.
2. Clarke, E. F. (2001). Meaning and the specification of motion in music. Musicae Scientiae, 5, 213-234.
3. Dannenberg, R. B., & Mont-Reynaud, B. (1987). Following a jazz improvisation in real time. In Proceedings of the 1987 International Computer Music Conference (pp. 241-248). San Francisco: International Computer Music Association.
4. Desain, P., & Honing, H. (1989). The quantization of musical time: A connectionist approach. Computer Music Journal, 13, 56-66.
5. Desain, P., & Honing, H. (1992). The quantization problem: Traditional and connectionist approaches. In M. Balaban, K. Ebcioglu, & O. Laske (Eds.), Understanding music with AI: Perspectives on music cognition (pp. 448-463). Cambridge, MA: MIT Press.
6. Desain, R, & Honing, H. (2003). The formation of rhythmic categories and metric priming. Perception, 32, 341-365.
7. Desain, R, Honing, H., Thienen, H. van, & Windsor, W. L. (1998). Computational modeling of music cognition: Problem or solution? Music Perception, 16, 151-166.
8. Eitan, Z., & Granot, R. Y. (2005). How music moves: Musical parameters and listeners' images of motion. Music Perception, 23, 221-247.
9. Epstein, D. (1994). Shaping time. New York: Schirmer.
10. Feldman, J., Epstein, D., & Richards, W. (1992). Force dynamics of tempo change in music. Music Perception, 10, 185-204.
11. Fodor, J. (2000). The mind doesn't work that way. The scope and limits of computational psychology. Cambridge, MA: MIT Press.
12. Friberg, A., & Sundberg, J. (1999). Does music performance allude to locomotion? A model of final ritardandi derived from measurements of stopping runners. Journal of the Acoustical Society of America, 105, 1469-1484.
13. Gabrielsson, A. (1999). Music performance. In D. Deutsch (Ed.), Psychology of music (2nd ed., pp. 506-602). New York: Academic Press.
14. Gjerdingen, R. (1994). Apparent motion in music? Music Perception, 11, 335-370.
15. Honing, H. (2003). The final ritard: On music, motion, and kinematic models. Computer Music Journal, 27, 66-72.
16. Honing, H. (2005). Is there a perception-based alternative to kinematic models of tempo rubato? Music Perception, 23, 79-85.
17. Hudson, R. (1996). Stolen time: History of tempo rubato. London: Clarendon Press.
18. Jacobs, A. M., & Grainger, J. (1994). Models of visual word recognition - Sampling the state of the art. Journal of Experimental Psychology: Human Perception and Performance, 29, 1311-1334.
19. Kronman, U., & J. Sundberg (1987). Is the musical ritard an allusion to physical motion? In A. Gabrielsson (Ed.), Action and perception in rhythm and music: No. 55 (pp. 57-68). Stockholm: Royal Swedish Academy of Music.
20. Large, E. W., & Kolen, J. F. (1994). Resonance and the perception of musical meter. Connection Science, 6, 177-208.
21. Lerdahl, F., & Jackendofe, R. (1983). A generative theory of tonal music. Cambridge, MA: MIT Press.
22. Longuet-Higgins, H. C. (1987). Mental processes: Studies in cognitive science. Cambridge, MA: MIT Press.
23. Longuet-Higgins, H. C., & Lisle, E. R. (1989). Modelling music cognition. Contemporary Music Review, 3, 15-27.
24. McAuley, J. D. (1995). Perception of time as phase: Towards an adaptive oscillator model of rhythmic pattern processing. Unpublished doctoral dissertation, Indiana University, Bloomington.
25. Michon, J. A. (1964). Studies on subjective duration 1. Differential sensitivity on the perception of repeated temporal intervals. Acta Psychologica, 22, 441-450.
26. Narmour, E. (1992). The analysis and cognition of basic melodic complexity: The implication-realization model. Chicago: Chicago University Press.
27. Pitt, M. A., & Myung, I. J. (2002). When a good fit can be bad. Trends in Cognitive Science, 6, 421-425.
28. Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472-491.
29. Pylyshyn, Z. W. (1984). Computation and cognition: Toward a foundation for cognitive science. Cambridge, MA: MIT Press.
30. Repp, B. H. (1992). Diversity and commonality in music performance: An analysis of timing microstructure in Schumann's "Träumerei." Journal of the Acoustical Society of America, 92, 2546-2567.
31. Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
32. Roberts, S., & Pashler, H. (2002). Reply to Rodgers and Rowe (2002). Psychological Review, 109, 605-607.
33. Roberts, S., & Sternberg, S. (1993). The meaning of additive reaction-time effects: Test of three alternatives. In D. E. Meyer & S. Kornblum (Eds.), Attention and performance XIV: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience - A silver jubilee (pp. 611-653). Cambridge, MA: MIT Press.
34. Rodgers, J. L., & Rowe, D. C. (2002). Theory development should begin (but not end) with good empirical fits: A comment on Roberts and Pashler (2000). Psychological Review, 109, 599-604.
35. Sove, P., & Repp, B. H. (1995). Musical motion and performance: Theoretical and empirical perspectives. In J. Rink (Ed.), The practice of performance (pp. 55-83). Cambridge, UK: Cambridge University Press.
36. Sundberg, J., & Verillo, V. (1980). On the anatomy of the ritard: A study of timing in music. Journal of the Acoustical Society of America, 68, 772-779.
37. Todd, N. P. M. (1985). A model of expressive timing in tonal music. Music Perception, 3, 33-58.
38. Todd, N. P. M. (1992). The dynamics of dynamics: A model of musical expression. Journal of the Acoustical Society of America, 91, 3540-3550.
39. Todd, N. P. M. (1995). The kinematics of musical expression. Journal of the Acoustical Society of America, 97, 1940-1949.
40. Todd, N. P. M. (1999). Motion in music: A neurobiological perspective. Music Perception, 17, 115-126.
41. Toiviainen, P. (1998). An interactive MIDI accompanist. Computer Music Journal, 22, 63-75.
42. Truslit, A. (1938). Gestaltung und Bewegung in der Musik [Shaping and motion in music]. Berlin-Lichtenfelde: Chr, Fiedrich Vieweg.