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The role of surprise in theory testing: Some preliminary observations

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.