Honing, H. (2007). Preferring the best fitting, least flexible, and most surprising prediction: Towards a Bayesian approach to model selection in music cognition. Proceedings of the Society for Music Perception and Cognition (SMPC)


While for most scientists the limitations of evaluating a computational model by showing a good fit with the empirical data (think of percentage correct, percentage variance accounted for, or minimizing error) are clear cut, a recent discussion (cf. Honing, 2006) shows that this wide spread method is still (or again) in the center of scientific debate. In the current paper, a Bayesian approach to model selection in music cognition is proposed that tries to capture the common intuition that a model's validity should increase when it correctly predicts an unlikely event, rather than when it correctly predicts something that was expected anyway.

One of the strengths of the computational modeling approach to music cognition is that, while a computational model may be designed and fine-tuned to explain one particular phenomenon, it has the added advantage that it can, in principle, also say something about the consequences for a related cognitive phenomenon. For example, it was shown in Honing (2006) that a model, that was designed to capture categorization in rhythm perception, can also be used to make predictions on the perception of ritardandi in music: i.e. how much slowing down (or speeding up) still allows for an appropriate categorization of the performed rhythm. Interestingly, this was not what the model was designed for to predict. However, calculating the predictions of this model on the possible shapes of final ritardandi turned out to be relatively surprising. In general, we would like to argue that the amount of surprise in a model's predictions is more relevant to a model's validity than one that simply makes a good fit with the data it was designed to fit.

In order to give some structure to the notion of what a surprising prediction of a model of music cognition may be, a distinction will be made between possible, plausible, and predicted observations, using a recent case study (Honing, 2006). These three notions will be used as a starting point to define three hypotheses: H-possible, H-plausible and H-predicted, each describing a surface (or intersection) of the predictions made by a model in a Bayesian framework (cf. Sadakata, Desain & Honing, 2006). As an example, a first, yet crude attempt to define a measure of surprise is to select the model that minimizes the intersection of H- predicted with respect to H-plausible, while preferring the H-predicted that is least smooth. As such, we will prefer a model that 1) fits the empirical data well (best fit), 2) makes limited range predictions (least flexible), in addition to making 3) non-smooth, unexpected predictions (most surprising).

Honing, H. (2006). Computational modeling of music cognition: a case study on model selection. Music Perception, 24(1), 365-376.
Sadakata, M, Desain, P., & Honing, H. (2006). The Bayesian way to relate rhythm perception and production. Music Perception, 23(3), 267-286.