Pitfalls in the modeling of developmental systems

Computational models of development aim to describe the mechanisms that underlie the acquisition of new skills or the emergence of new capabilities. The strength of a model is judged by both its ability to explain the phenomena in question as well as its ability to generate new hypotheses, generalize to new situations, and provide a unifying conceptual framework. Although often constructed using traditional engineering methodologies, evaluating the performance of a computational model of development in terms of traditional perspectives is a flawed approach. This paper addresses the fundamental issues that confound quantitative analysis of computational models of developmental systems. In particular, we focus on the following recommendations: (i) do not equate the success of a developmental model with its peak performance at some task; (ii) do not employ purely subjective or vague measures of model fitness; and (iii) do not hide or reject variation as found in the computational model. Along the way, we discuss the aspects of computational models of development that lead to the requirements for specialized methods of analysis.