Honing, H. (2006). The role of surprise in theory testing: Some preliminary observations. Proceedings of the International Conference on Music Perception and Cognition (pp. 38-42). Bologna: Italy


While the most common way of evaluating a computational model is by showing a good fit with the empirical data, recently the literature on theory testing and model selection criticizes the assumption that this is actually strong evidence for a model. This paper explores the possibilities of developing a method selection technique that can serve as an alternative to a goodness-of-fit (GOF) measure. This alternative, a measure of surprise, is based on the common idea that a model gets more support from the correct prediction of an unlikely event than the correct prediction of something that was expected anyway.

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