This research area concerns individual variations in how people perceive, process, and engage with music. Some research examines participants' abilities to perform specific musical tasks, such as remembering melodies or tapping to beats, while other research investigates more subjective aspects of music cognition, like preferences for particular harmonies or extramusical associations for different timbres.
Individual differences in music appreciation
A recent focus at the CMS is the study of individual differences in music appreciation. Previous research in this area has typically taken a group-level approach, comparing for example the preferences of musically trained participants with those of non-musicians. We instead take a more fine-grained approach, using unsupervised-learning methods that allow us to treat each participant on their own terms and thereby identify a richer varieties of response patterns. For instance, Joshua Frank (PhD, 2023-) is currently applying these techniques to investigate individual differences in the perceived pleasantness of musical chords.
Individual differences in musical abilities
We have developed numerous computer-based tests of musical abilities, many of which are now used in LongGold, a large-scale longitudinal study examining musical, academic, and social development in children. These tests assess abilities such as melodic memory, beat perception, and tuning perception. They employ adaptive techniques to tailor task difficulty to each participant's performance level, allowing the tests to accommodate a broad range of abilities. These tests are available to try on the LongGold website.
Recent students at the CMS have contributed to developing new tests and test batteries. For example, Jessica Clayton (undergraduate, 2022-23) developed a test of the ability to identify individual notes within chords, while David Whyatt (MPhil, 2023-4) created a comprehensive test battery exploring various aspects of pitch perception.
These tests serve multiple purposes. They support research into the cognitive and neural foundations of musical abilities, with correlation patterns between different musical abilities helping to reveal how these skills rely on both shared and distinct cognitive processes. Additionally, they enable us to study how musical abilities develop over time, and how this depends on factors such as family background and school resources.
Moreover, these musical tests can often be adapted into training applications. Rather than assessing ability, these applications provide opportunities for focused practice with immediate feedback. Adaptive algorithms are particularly valuable in this context, ensuring that practice tasks are neither too easy nor too difficult.
Example publications
Eitel, M., Ruth, N., Harrison, P., Frieler, K., & Müllensiefen, D. (2024). Perception of chord sequences modeled with prediction by partial matching, voice-leading distance, and spectral pitch-class similarity: A new approach for testing individual differences in Harmony perception. Music & Science, 7, 20592043241257654. https://doi.org/10.1177/20592043241257654
Harrison, P. M. C., 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(1), 3618. https://doi.org/10.1038/s41598-017-03586-z
Harrison, P. M. C., & Müllensiefen, D. (2018). Development and Validation of the Computerised Adaptive Beat Alignment Test (CA-BAT). Scientific Reports, 8(1), 12395. https://doi.org/10.1038/s41598-018-30318-8
Larrouy-Maestri, P., Harrison, P. M. C., Walker, D., & Müllensiefen, D. (2017). A new test of the ability to detect mistuning in real music. In E. Van Dyck (Ed.), Proceedings of the 25th Anniversary Conference of the European Society for the Cognitive Sciences of Music (pp. 189–190).
The LongGold project
The LongGold project is a longitudinal project on musical development that collects data on musical listening ability as well as on cognitive abilities, psychosocial skills, leisure time activities, and a wide range of background variables from secondary school students on an annual basis. The LongGold team numbers more than 10 collaborators, led by Daniel Müllensiefen, and including Peter Harrison at the CMS. Since its inception in 2015, the project has collected data from 13 schools in the UK and Germany; recently, the project expanded to Italy and Latvia. Altogether data from around 7000 participants has been collected. Participants are recruited from year 7 to year 12 (in the UK) and range in age from 10 to 18.
Several aspects of the large dataset collected on the project so far have not yet been explored. This includes, for example, the influence of a musical home environment on musical development or a comparison of sports, music, and drama as leisure activities and their effect on musical, cognitive, psychosocial, and academic development. Further, the role of individual psychosocial variables (e.g. Grit, Duckworth et al., 2007, or Hope, Snyder et al., 2007, or School Engagement, Wang et al., 2011) for musical development still needs exploration. In addition, music performance anxiety has recently been included as a new measure and its relationship to other variables can now be investigated. Methodologically, the longitudinal aspects of the data still need to be explored with state-of-the-art methods for longitudinal analysis that can target causal relationships, such as random-intercept cross-lagged panel models (Usami et al., 2019), dual-change score model (Wiedemann et al., 2022), or longitudinal g-computation (Hernan & Robbins, 2020).
The project page provides an overview of the project, particular previous publications and a list of demo applications of the employed tests and questionnaires.
We welcome prospective students and researchers who are interested in joining the LongGold project. They would typically choose a research question that can be tackled using the large existing dataset, but there would also be opportunities to propose new test components that can be included in future iterations of the study. Peter and Daniel would co-supervise the project.
For more detailed overviews of the project, see Müllensiefen & Harrison (2021) and Müllensiefen et al. (2022).
For an example student-led project exploiting the LongGold data, see Anstee et al. (2023).
Relevant publications
Anstee, L., Müllensiefen, D., & Harrison, P. M. C. (2023). Handedness and musicality in secondary school students. Music Perception, 40(5), 373–390. https://doi.org/10.1525/mp.2023.40.5.373
Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087
Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Müllensiefen, D., & Harrison, P. (2021). The impact of music on adolescents' cognitive and socio-emotional learning. In J. Harrington, J. Beale, A. Fancourt, & C. Lutz (Eds.), The "BrainCanDo" handbook of teaching and learning (pp. 222–239). Routledge.
Müllensiefen, D., Elvers, P., & Frieler, K. (2022). Musical development during adolescence: Perceptual skills, cognitive resources, and musical training. Annals of the New York Academy of Sciences, 1518(1), 264–281. https://doi.org/10.1111/nyas.14911
Snyder, C. R., Hoza, B., Pelham, W. E., Rapoff, M., Ware, L., Danovsky, M., Highberger, L., Rubinstein, H., & Stahl, K. J. (1997). The development and validation of the Children's Hope Scale. Journal of Pediatric Psychology, 22(3), 399–421. https://doi.org/10.1093/jpepsy/22.3.399
Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods, 24(5), 637–657. https://doi.org/10.1037/met0000210
Wang, M.-T., Willett, J. B., & Eccles, J. S. (2011). The assessment of school engagement: Examining dimensionality and measurement invariance by gender and race/ethnicity. Journal of School Psychology, 49(4), 465–480. https://doi.org/10.1016/j.jsp.2011.04.001
Wiedemann, M., Thew, G., Košir, U., & Ehlers, A. (2022). lcsm: An R package and tutorial on latent change score modelling. Wellcome Open Research, 7. https://doi.org/10.12688/wellcomeopenres.17536.1
