Dr. Daniel Müllensiefen from the Music, Mind and Brain research group at Goldsmiths, London visits MIB.

04.10.2017 | Hella Kastbjerg

TITLE:

The psychology of musical experience from three angles: Stimulus features, psychometrics and behavioral economics

ABSTRACT:

Describing aesthetics experiences with scientific models is a complex task, but there seems to be an emerging consensus that three main areas contribute to psychological experiences of aesthetic stimuli: 1) elements of the aesthetic object itself, 2) characteristics of the person, and 3) situational as well as contextual information. Taking music as an example domain we will demonstrate rigorous approaches that allow the construction of empirical models within each of the three areas. 1) We suggest that the comprehensive computational analysis of musical features can be employed to describe relationships with psychological responses (Jakubowski, Müllensiefen & Stewart, 2016; van Balen et al., 2015). In addition we will discuss new strategies going beyond mere feature analysis to gain a deeper understanding of how musical elements  are causally linked to perceptual and cognitive responses. 2) For assessing characteristics of the individual we advocate the use of modern psychometric techniques such as item response theory, automatic item generation and adaptive testing (e.g. Harrison, Collins & Müllensiefen, 2017). 3) In addition, we’ll present experimental approaches (Anglada-Tort & Müllensiefen, 2017) inspired by paradigms from behavioral economics (Kahneman, 2011) that aim to assess the degree to which biases and heuristics introduced through the situational context affect human judgements of musical stimuli.
The unifying conceptual bracket of these approaches is a thorough understanding of causality (Pearl, 2009) and its implementation in experimental and observational research. Finally, we’ll make sure the talk will be more fun than this abstract is able to convey!

References:

Anglada-Tort & Müllensiefen, D. (2017). The repeated recording illusion: The effects of extrinsic and individual difference factors on musical judgements. Music Perception 35(1), 92-115. DOI: https://doi.org/10.1525/MP.2017.35.1.92

Harrison, P., Collins, T., & Müllensiefen, D. (2017). Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. Scientific Reports, 7. DOI: 10.1038/s41598-017-03586-z

Jakubowski, K., Finkel, S., Stewart, L. & Müllensiefen, D. (2016). Dissecting an Earworm: Melodic Features and Song Popularity Predict Involuntary Musical Imagery. Psychology of Aesthetics, Creativity, and the Arts, 3. doi: http://dx.doi.org/10.1037/aca0000090.

KAHNEMAN, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.

Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press. 2nd edition.

van Balen, J., Burgoyne, J.A., Bountouridis, D., Müllensiefen, D. & Veltkamp, R. (2015). Corpus Analysis Tools for Computational Hook Discovery. In M. Müller & F. Wiering (Eds.) Proceedings of the 16th International Society for Music Information Retrieval (ISMIR) Conference 2014, Malaga, Spain. (pp. 227-233).