Studies in clinical psychology, especially abnormal psychology, typically use null hypothesis statistical testing to examine behavioral data that were gathered through cognitive tasks. This paper identifies the problems associated with using conventional research methods and discusses the advantages of using a computational approach to examine mental disorders. The computational approach includes cognitive modeling that estimates latent parameters that cannot be directly observed from behavioral data. Cognitive modeling makes it possible to explain and predict behavioral data in a logically valid way and contributes to the assessment of mental disorders. This paper introduces the best practices in cognitive modeling by using probabilistic reversal learning task as an example. In conclusion, this paper provides examples of research studies that use cognitive modeling in clinical psychology and discusses future directions, including parameter estimation by hierarchical Bayesian estimation and hierarchical Bayesian inference model as a cognitive model.
