Enhancing evaluation of reward learning using computational modeling methods

点击翻译按钮获取中文摘要

Studies of frequently rely on behavioral tasks as means of understanding etiological processes, correlates, and consequences of psychopathology. In the context of reward learning and decision making, the Iowa Gambling Task (IGT) is frequently used. Ultimately, studies routinely rely on the original implementation of the task that permits participants to fully direct the exploration and learning in the task. This reduces researchers' ability to distinguish reward and punishment learning. Moreover, despite multiple processes being involved in IGT performance, the task is frequently summarized as a single performance metric (i.e., proportion of plays on advantageous/disadvantageous stimuli). More modern approaches, including computational modeling, provides a means to distinguish between processes (e.g., reward learning, punishment learning, reward sensitivity, perseveration tendency) that are of interest to substantive research questions about altered functioning in psychopathology. A critical limitation of the field is that studies that have used computational modeling approaches with the IGT have only used the original version of the task. A stronger experimental paradigm may increase the precision of the estimated parameters. Moreover, computational modeling studies of the IGT, particularly in developmental samples, rely on cross-sectional designs that precludes examining test-retest reliability or longitudinal change. This project will estimate computational modeling of the IGT using an updated version of the task that has full experimental control of the stimulus presentation across all trials. In independent samples (Study 1 n = 50 undergraduates; Study 2 [R01 MH107495] offspring and parents (n = 248), the IGT was administered on multiple occasions (Study 1 twice, approximately 4 weeks apart; Study 2 up to five occasions, approximately 9 months apart for offspring and parents). Using data from Study 1, we will develop reward learning computational models to describe task behavior in the updated version of the IGT. Using data from Study 2, we will examine the generalizability of the model to independent samples. We will examine test-retest reliability of performance using the computational modeling approaches, but expand the number of repeated assessments. We will also examine the validity of model parameters against indices of reward functioning measures (including self-reports in both offspring and parents; brain function in offspring; and family history of depression). Finally, we will also adapt the computational model to include indices of development to model changes in the offspring sample. In addition to the substantive contributions to the assessment of reward learning and decision making by these task and modeling adaptations, we will also use our work to aid in the dissemination of these models by updating open source software (the hBayesDM package) to include our models for use by other investigators.