Computational modeling of music cognition: A case study on model selection
Henkjan Honing

Abstract:
While the most common way of evaluating a computational model is to
see whether it shows a good fit with the empirical data, recent
literature on theory testing and model selection criticizes the
assumption that this is actually strong evidence for the validity of a
model. This paper presents a case study from music cognition (modeling
the ritardandi in music performance) and compares two families of
computational models (kinematic and perceptual) using three different
model selection criteria: goodness-of-fit, model's simplicity,
and the degree of surprise in the predictions. In the light of what
counts as strong evidence for a model's validity - namely
that it makes precise, non-smooth, and relatively surprising
predictions - the perception-based model is preferred over the
kinematic model.