A Predictive Coding Approach to Estimating the Complexity of Popular Music Drum Patterns
Olivier Senn · 2022
This study presents a method to estimate the complexity of popular music drum patterns based on a core idea from predictive coding. Specifically, it postulates that the complexity of a drum pattern depends on the quantity of surprisal it causes in the listener. Surprisal, according to predictive coding theory, is a numerical measure that takes large values when the perceiver’s inner model of the surrounding world fails to predict the actual stream of sensory data (i.e. when the perception surprises the perceiver), and low values if model predictions and sensory data agree. The proposed new method first approximates a listener’s inner model of a popular music drum pattern (using ideas on inculturation and a Bayesian learning process). It then quantifies the listener’s surprisal evaluating the discrepancies between the predictions of the inner model and the actual drum pattern. It finally estimates drum pattern complexity from surprisal. The method was optimised and tested using a set of forty popular music drum patterns, for which empirical perceived complexity measurements are available. The new method provided complexity estimates that had a good fit with the empirical measurements (R2=.852). This suggests that surprisal indeed captures essential aspects of complexity, and that it may serve as the basis for a general theory of perceived complexity.