A predictive coding approach to modelling the perceived complexity of popular music drum patterns

Abstract

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 internal 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 internal model of a popular music drum pattern (using ideas on enculturation and a Bayesian learning process). It then quantifies the listener's surprisal evaluating the discrepancies between the predictions of the internal 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). The method was implemented as an R script that can be used to estimate the complexity of popular music drum patterns in the future. Simulations indicate that we can expect the method to predict perceived complexity with a good fit (R2≥.709) in 99% of drum pattern sets randomly drawn from the Western popular music repertoire. These results suggest that surprisal indeed captures essential aspects of complexity, and that it may serve as a basis for a general theory of perceived complexity.