1 Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany; Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; International Max Planck Research School LIFE, Berlin, Germany. Electronic address: vandenbos@mpib-berlin.mpg.de.
2 Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany; International Max Planck Research School LIFE, Berlin, Germany.
3 Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, USA.
4 Center for Cognitive and Decision Sciences, Department of Psychology, University of Basel, Basel, Switzerland.
5 Department of Psychology, Concordia University, Montreal, Canada; Department of Psychology, TU Dresden, Dresden, Germany. Electronic address: ben.eppinger@concordia.ca.

In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development.