A formal comparison of model variants for performance prediction

In the field of cognitive science, the primary means of judging a model’s viability is made on the basis of goodness-of-fit between model and human empirical data. Recent developments in model comparison reveal, however, that other criteria should be considered in evaluating the quality of a model. These criteria include model complexity, generalizability, predictive capability, and of course descriptive adequacy. The current investigation seeks to formally compare three variants of a mathematical model for performance prediction. The results raise the issue of how to go about selecting a model when formal comparison methods reveal equivalent values. A possibility briefly proposed at the end of the paper is that cognitive/neural plausibility is an appropriate tiebreaker among otherwise equivalent functional forms