Learning a Song: an ACT-R Model

Authors: Belkacem Chikhaoui, Helene Pigot, Mathieu Beaudoin, Guillaume Pratte, Philippe Bellefeuille, Fernando Laudares

Abstract:

The way music is interpreted by the human brain is a very interesting topic, but also an intricate one. Although this domain has been studied for over a century, many gray areas remain in the understanding of music. Recent advances have enabled us to perform accurate measurements of the time taken by the human brain to interpret and assimilate a sound. Cognitive computing provides tools and development environments that facilitate human cognition simulation. ACT-R is a cognitive architecture which offers an environment for implementing human cognitive tasks. This project combines our understanding of the music interpretation by a human listener and the ACT-R cognitive architecture to build SINGER, a computerized simulation for listening and recalling songs. The results are similar to human experimental data. Simulation results also show how it is easier to remember short melodies than long melodies which require more trials to be recalled correctly.

Keywords: Computational model, cognitive modeling, simulation, learning, song, music.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080670

References:

[1] G. D. Sawa, "Oral transmission in arabic music, past and present," Oral Tradition Journal, vol. 4/1-2, pp. 254-265, 1989.
[2] A. Racette and I. Peretz, "Learning lyrics: to sing or not to sing?" Memory & Cognition Journal, vol. 35 (2), pp. 242-253, 2007.
[3] J. W. Stansell, "The use of music in learning languages: A review of the literature," University of Illinois at Urbana-Champaign, M.Ed., 2005.
[4] M. L. Huy, "The role of music in second language learning: A vietnamese perspective," in Conference of the Australian Association for Research in Education and the New Zealand Association for Research in Education, 1999.
[5] G. Mather, Foundations of Perception. Psychology Pr, 2006.
[6] C.-H. Chouard, L-oreille musicienne. Les chemins de la musique de l-oreille au cerveau. Gallimard, 2001.
[7] J. Plantinga and L. J. Trainor, "Memory for melody: infants use a relative pitch code," Cognition Journal, vol. 98, pp. 1-11, 2004.
[8] W. Gruhn and F. H. Rauscher, The Neurobiology of Learning: New Approaches to Music Pedagogy Conclusions and Implications, nova ed., ser. Neurosciences in Music Pedagogy, 2007, ch. 10, pp. 263-295.
[9] V. J. Williamson, A. D. Baddeley, and G. J. Hitch, "Music in working memory? examining the effect of pitch proximity on the recall performance of nonmusicians." 9th International Conference on Music Perception and Cognition, pp. 1581-1590, 2006.
[10] J. R. Anderson, D. Bothell, M. D. Byrne, S. Douglass, C. Lebiere, and Y. Qin, "An integrated theory of the mind," Psychological Review, vol. 111, 136-1060, 2004.
[11] J. R. Anderson, N. A. Taatgen, and M. D. Byrne, "Learning to achieve perfect time sharing: Architectural implications of hazeltine, teague ivry (2002)," Journal of Experimental Psychology: Human Perception and Performance, vol. 31, No. 4, pp. 749-761, 2005.
[12] M. D. Byrne, "Act-r/pm and menu selection: Applying a cognitive architecture to hci," International Journal of Human-Computer Studies, vol. 55, pp. 41-84, 2001.
[13] D. Bothell, "Act-r 6.0 reference manual," Carnegie Mellon University, Working Draft, 2004.
[14] J. R. Anderson, D. Bothell, C. Lebiere, and M. Matessa, "An integrated theory of list memory," Journal of Memory and Language, vol. 38, no. 4, pp. 341-380, 1998.
[15] I. Peretz and R. Zatorre, "Brain organization for music processing," Annual Review of Psychology, vol. 56, pp. 89-114, 2005.
[16] N. Gaab, C. Gaser, T. Zaehle, L. Jancke, and G. Schlaug, "Functional anatomy of pitch memory-an fmri study with sparse temporal sampling," NeuroImage, vol. 19, no. 4, p. 1417, 2003.
[17] U. Will, "Oral memory in australian song performance and the parrykirk debate: a cognitive ethnomusicological perspective," International Study Group on Music Archaeology, vol. X, pp. 1-29, 2004.
[18] B. Chikhaoui and H. Pigot, "Analytical model based evaluation of human machine interfaces using cognitive modeling," International Journal of Information Technology, vol. 4, no. 4, pp. 252-261, 2008.
[19] B. E. John and D. D. Salvucci, "Multi-purpose prototypes for assessing user interfaces in pervasive computing systems," IEEE pervasive computing, vol. 4, no. 4, pp. 27-34, 2005.
[20] B. Chikhaoui and H. Pigot, "Simulation of a human machine interaction: Locate objects using a contextual assistant," in procedeengs of the 1st International North American Simulation Technology Conference, 2008, pp. 75-80.