Abstract
Music psychologists have long been concerned with phenomena such as repetition and tonality – both the internal representations formed by exposure to these phenomena, and how these representations vary with expertise. A question arises of whether less expert listeners can gain proficiency in perceiving a particular phenomenon by being exposed to representations that are known to be employed by more expert listeners. The current paper addresses this question within the domain of music appraisal. Participants with varying levels of musical expertise interacted with visualizations of two excerpts from Beethoven’s symphonies. One visualization (ScoreViewer) showed the staff notation of the music, synchronized to an orchestral recording. The other (PatternViewer) also depicted the notes synchronized to the recording, as well as representations of the music’s repetitive and tonal structure. Participants’ appraisal skills were assessed via multiple-choice questions on instrumentation, dynamics, repetition, and tonality. Results indicated that interacting with the PatternViewer visualization led to a significant improvement in listeners’ appraisal of repetitive and tonal structure, compared to interacting with the ScoreViewer. The size of this effect was well predicted by amount of formal musical training, such that less expert listeners exhibited larger improvements than more expert listeners. While further work is required to determine whether the observed effects transfer beyond the pieces studied or into long-term learning, these findings for appraisal skills indicate that carefully chosen representations from models of expert behavior can, in turn, help less expert individuals to improve their understanding of musical phenomena.
M. Grachten and H. Frostel—Independent Machine Learning Consultant.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
Both were from recordings of the Royal Concertgebouw Orchestra. The first was conducted by David Zinman and the second by Iván Fischer.
- 4.
Only one participant was at ceiling for repetitive/tonal questions in the ScoreViewer, and two other participants were at ceiling for repetitive/tonal questions in the PatternViewer, so these comparisons are not being confounded by ceiling effects.
References
AQA: listening to and appraising music: past paper for GCSE music unit 1. AQA, Manchester, UK (2012). Accessed 13 Jan 2014. https://www.aqa.org.uk/
Brady, T.F., Konkle, T., Alzarez, G.A.: Compression in visual working memory: using statistical regularities to form more efficient memory representations. J. Exp. Psychol. Gen. 138(4), 487–502 (2009)
Brainmetrix: brain training, brain exercises, brain fitness games, brain metrix (2014). Accessed 1 Aug 2014. https://www.brainmetrix.com/
Chabris, C.F.: Prelude or requiem for the Mozart effect? Nature 400, 826–827 (1999)
Chen, N.S., Hwang, G.J.: Transforming the classrooms: innovative digital game-based learning designs and applications. Education Tech. Research Dev. 62, 125–128 (2014)
Collins, T., Arzt, A., Flossmann, S., Widmer, G.: SIARCT-CFP: improving precision and the discovery of inexact musical patterns in point-set representations. In: Proceedings of the International Society for Music Information Retrieval Conference, pp. 549–554. Curitiba, Brazil (2013)
Collins, T., Meredith, D.: Maximal translational equivalence classes of musical patterns in point-set representations. In: Proceedings of Mathematics and Computation in Music, pp. 88–99. Montreal, Canada (2013)
Collins, T., Tillmann, B., Barrett, F.S., Delbé, C.: A combined model of sensory and cognitive representations underlying tonal expectation: from audio signals to behavior. Psychol. Rev. 121(1), 33–65 (2014)
Cook, N.: The perception of large-scale tonal closure. Music. Percept. 5(2), 197–205 (1987)
Deutsch, D.: The processing of structured and unstructured tonal sequences. Percept. Psychophys. 28, 381–389 (1980)
Deutsch, D., Feroe, J.: The internal representation of pitch sequences in tonal music. Psychol. Rev. 88(6), 503–522 (1981)
Edexcel: listening and appraising: past paper for GCSE music unit 3. Pearson Education Ltd., London, UK (2012). Accessed 13 Jan 2014. https://www.edexcel.com/
Grachten, M., Gasser, M., Arzt, A., Widmer, G.: Automatic alignment of music performances with structural differences. In: Proceedings of the International Society for Music Information Retrieval Conference, pp. 607–612. Curitiba, Brazil (2013)
Hmelo-Silver, C.E., Pfeffer, M.G.: Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cogn. Sci. 28, 127–138 (2004)
Katsiavalos, A., Collins, T., Battey, B.: An initial computational model for musical schemata theory. In: Proceedings of the International Society for Music Information Retrieval Conference, pp. 166–172. Delft, The Netherlands (2019)
Kraus, N., Martin, M.: Biological impact of musical training in at-risk children. In: Proceedings of the American Psychological Association’s 122nd Annual Convention. APA, Washington, DC (2014). Accessed 8 Aug 2014. https://www.apa.org/news/press/releases/2014/08/musical-training.aspx
Krumhansl, C.L.: Cognitive foundations of musical pitch. Oxford University Press (1990)
Krumhansl, C.L., Kessler, E.J.: Tracing the dynamic changes in perceived tonal organization in a spatial representation of musical keys. Psychol. Rev. 89(4), 334–368 (1982)
Malinowski, S.A.: Time-line of the music animation machine (and related experiments) (1985). Accessed 23 May 2014. https://www.musanim.com/mam/mamhist.htm
Newton, I.: Opticks. Dover (1952). (Original work published 1704)
OCR: listening: past paper for GCSE music unit 4. OCR, Coventry, UK (2012). Accessed 13 Jan 2014. https://www.ocr.org.uk/
Owen, A.M., et al.: Putting brain training to the test. Nature 465(7299), 775–778 (2010)
Rauscher, F.H., Shaw, G.L., Ky, K.N.: Music and spatial task performance. Nature 365(64478), 611 (1999)
Saffran, J.R., Johnson, E.K., Aslin, R.N., Newport, E.L.: Statistical learning of tone sequences by human infants and adults. Cognition 1(70), 27–52 (1999)
Sutcliffe, R., Hovy, E., Collins, T., Wan, S., Crawford, T., Root, D.L.: Searching for musical features using natural language queries: the C@merata evaluations at MediaEval. Lang. Resour. Eval. 53(1), 87–140 (2019)
Tillmann, B., Bharucha, J.J., Bigand, E.: Implicit learning of tonality: a self-organizing approach. Psychol. Rev. 4(107), 885–913 (2000)
WJEC: appraising: past paper for GCSE music unit 3. WJEC CBAC Ltd, Cardiff, UK (2012). Accessed 13 Jan 2014. https://www.wjec.co.uk/
Zhang, W., Luck, S.J.: Discrete fixed-resolution representations in visual working memory. Letters, Nature 453(7192), 233–235 (2008)
Zhang, W., Luck, S.J.: The number and quality of representations in working memory. Psychol. Sci. 22(11), 1434–1441 (2011)
Acknowledgments
Gerhard Widmer’s work is supported by the European Research Council (ERC) under the EU’s Horizon 2020 research and innovation programme, grant agreement No. 101019375 (“Whither Music?”).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nikrang, A., Grachten, M., Gasser, M., Frostel, H., Widmer, G., Collins, T. (2023). Music Visualisation and Its Short-Term Effect on Appraisal Skills. In: Mori, H., Asahi, Y., Coman, A., Vasilache, S., Rauterberg, M. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14056. Springer, Cham. https://doi.org/10.1007/978-3-031-48044-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-48044-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48043-0
Online ISBN: 978-3-031-48044-7
eBook Packages: Computer ScienceComputer Science (R0)