This paper reviews progress in the application of computational models to personality, developmental, and clinical neuroscience. We first describe the concept of a computational phenotype, a collection of parameters derived from computational models fit to behavioral and neural data. This approach represents individuals as points in a continuous parameter space, complementing traditional trait and symptom measures. One key advantage of this representation is that it is mechanistic: The parameters have interpretations in terms of cognitive processes, which can be translated into quantitative predictions about future behavior and brain activity. We illustrate with several examples how this approach has led to new scientific insights into individual differences, developmental trajectories, and psychopathology. We then survey some of the challenges that lay ahead.