This paper reviews progress in the application of computational models topersonality, developmental, and clinical neuroscience. We first describe theconcept of a computational phenotype, a collection of parameters derived fromcomputational models fit to behavioral and neural data. This approach representsindividuals as points in a continuous parameter space, complementing traditionaltrait and symptom measures. One key advantage of this representation is that itis mechanistic: The parameters have interpretations in terms of cognitiveprocesses, which can be translated into quantitative predictions about futurebehavior and brain activity. We illustrate with several examples how thisapproach has led to new scientific insights into individual differences,developmental trajectories, and psychopathology. We then survey some of thechallenges that lay ahead.
