TOWARD A HIGH DIMENSIONAL COMPUTATIONAL DESCRIPTION OF VARIATION IN HUMAN DECISION-MAKING ACROSS PSYCHIATRIC AND NON-PSYCHIATRIC POPULATIONS PI: Dr. John P. O'Doherty Institution: California Institute of Technology PROJECT SUMMARY Computational psychiatry (CP) promises to gain deeper explanatory insight into psychiatric disorders through the application of formal computational models to task-related behavioral data and brain measures. However, research in CP to date has mostly involved a narrow unidimensional focus, utilizing either a relatively limited set of computational constructs such as simple model-free (MF) reinforcement learning (RL), and/or restricted to studying a specific task, a specific disease, or even a particular model parameter. For CP to reach its potential, we need to broaden the field's scope. To achieve this, it is necessary to develop an integrated theory and formal framework supported by a task battery that will enable the quantification of individual differences across a range of computational mechanisms pertinent to the diagnosis and treatment of clinical disorders. The objective of the current proposal is to implement the initial groundwork needed to build and test a computational framework and task battery that can facilitate a multi-dimensional computational description of individual variability in parameters relevant for characterizing psychiatric dysfunction. We have constructed a computational assessment battery (CAB) consisting of four distinct yet inter- related tasks that move beyond simple RL to probe various aspects of learning, cognition, and decision-making. First, we assess learning about losses as well as rewards. Secondly, we measure model-based (MB) alongside simple MF learning and decision making, and the arbitration allocating control to either strategy. Thirdly, we examine strategies for solving the exploration/exploitation dilemma, in which individuals have to decide whether to exploit an option known to yield reward or explore an option whose outcomes are unknown. Finally, we assess social-learning, in which an individual can either infer the goals of another individual or simply imitate that agent's behavior. We propose to build an integrated computational model that can capture relevant computations being implemented in each of the tasks in our battery, alongside a hierarchical model-fitting and parameter estimation framework to enable us to retrieve reliable parameter estimates for each computational variable of interest. We will leverage common computational mechanisms engaged across our task battery to improve estimability and generalizability. We will then acquire behavioral data in a large on-line (n=1000), and modest (n=100) in-lab sample on the CAB to establish the internal validity and test/re-test reliability of our computational model and parameter estimates. Finally, we will explore the relationship between model-estimated parameter estimates from behavior on our task battery and var...