Building normative models of Reinforcement Learning Decision Making Behavior

点击翻译按钮获取中文摘要

The main objective in the field of psychiatry is to be able to treat patients at an individual level. To reach this goal of precision medicine, large scale initiatives such as the RDoC have been developed to find new ways to parse heterogeneity in psychiatric disorders. Their success has been slow partly due to a slow transition away from case-control studies based on diagnoses and limitations due to small sample size. Therefore, there is a critical need to find alternative solutions at an affordable cost. One strategy is to identify a complex behavior such as Reinforcement Learning based decision making (RLDM) that is impaired across various psychiatric disorders and adopt a computational framework to explain heterogeneity at an individual level. RLDM is a multifaceted construct involving several sub-processes ranging from estimating values of different options in the environment (valuation), accumulating evidence for these options (sequential sampling), choosing the best option (explore- exploit behavior), estimating the outcome value (salience attribution) and lastly integrating relevant information about outcomes and updating the value of stimuli (learning rate). These sub-processes can be quantified by utilizing computational models. However, prior to building normative models of these RLDM constructs that can be potentially utilized in clinical practice, it is critical to assess the reliability of these RLDM model-derived parameters to avoid translational failures. Therefore, our main goals of this proposal are to (1) parse RLDM sub- processes into mathematically-defined parameters in a large sample using a diverse set of tasks; (2) assess test-retest reliability of these parameters; and finally (3) build normative models of the parameters and chart the heterogeneity at the level of the individual. To achieve this goal, we will acquire behavioral data from six RL tasks, including delay and effort discounting, probabilistic learning, virtual patch foraging, Pavlovian instrumental transfer and approach-avoid conflict tasks. These tasks are each optimized to measure at least two of the RL sub-processes separately. We will collect behavioral data and self-report assessments from a community sample from 1000 participants (aged 18-85). To estimate test-retest reliability of RLDM parameters, we will invite 500 healthy participants from this sample to complete the session again in one week’s time. First, we will apply start- of-art computational models to quantify RLDM behavior in each subject. Second, we will calculate the test-retest reliability of these parameters. Third, we will build normative models to link each of the RLDM construct with age and calculate each subject’s deviation from the norm. Lastly, we will conduct soft clustering on these deviations to identify clusters and investigate their differences in psychopathology and general functioning.