The promises and pitfalls of applying computational models to neurological and psychiatric disorders

Computational models have become an integral part of basic neuroscience and have facilitated some of the major advances in the field. More recently, such models have also been applied to the understanding of disruptions in brain function. In this review, using examples and a simple analogy, we discuss the potential for computational models to inform our understanding of brain function and dysfunction. We argue that they may provide, in unprecedented detail, an understanding of the neurobiological and mental basis of brain disorders and that such insights will be key to progress in diagnosis and treatment. However, there are also potential problems attending this approach. We highlight these and identify simple principles that should always govern the use of computational models in clinical neuroscience, noting especially the importance of a clear specification of a model’s purpose and of the mapping between mathematical concepts and reality. Computational models are applied increasingly to the study of brain function and dysfunction. Teufel & Fletcher highlight the promise of this approach, but also some of the problems that can arise from the misapplication of such models. Using a simple analogy, they identify key principles necessary for avoiding common pitfalls.