School of Electronic Engineering and Computer Science

Probabilistic learning of musical syntax

Supervisor: Dr Marcus Pearce

Research group(s): Cognitive Science, Centre for Digital Music

This project will use a combination of computational modelling and empirical experiments with human participants to understand how listeners learn the syntactic structure of a musical style and how this learned syntax impacts on music perception and aesthetic experience. Our existing research suggests that statistical learning and probabilistic prediction are fundamental processes in music cognition. Predictions about musical events reflect a hierarchical process of top-down prediction at a range of different time-scales based on a learned syntactic model of the sensory environment. The model is acquired adaptively through exposure and the discrepancy between predicted and actual sensory input (the prediction error) is used to drive learning. Predictions are thought to be precision-weighted such that very certain (low entropy) predictions gain a higher weight than less certain (higher entropy) predictions. Within this general approach, there is scope to focus the project on different musical parameters (melody, rhythm, harmony, metre, tonality, form), different psychological processes (e.g., expectation, memory, segmentation, complexity, similarity, attention, stream segregation, emotion, aesthetics), different musical styles and cultures (in particular, non-Western musical styles), different populations of listeners (e.g., different age groups or listeners with different cultural backgrounds), different experimental methods (e.g., behavioural testing; Electroencephalography or EEG) and different computational approaches (e.g., structured probabilistic models, empirical Bayesian methods, neural networks). There is also scope within this framework to investigate non-musical auditory sequences.


Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016). Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns. Proceedings of the National Academy of Sciences, 113, E616-E625.

Pearce, M. T., Zaidel, D. W., Vartanian, O., Skov, M., Leder, M., Chatterjee, A., & Nadal, M. (2016). Neuroaesthetics: the cognitive neuroscience of aesthetic experience. Perspectives in Psychological Science, 11, 265-279.

Pearce, M. T. (2018). Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation. Annals of the New York Academy of Sciences, 1423, 378-395.