Computationally modeling individual differences in probabilistic decision-making across positive and negative valence domains

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PROJECT SUMMARY In our daily lives, we choose between different courses of action with the hope of achieving desired positive outcomes and avoiding feared negative outcomes; this is complicated by various forms of uncertainty that impact the probability that any given action will result in a particular outcome. Within NIMH’s RDoC framework, studies of the mechanisms involved in reward-based action valuation and choice have informed constructs listed under the Positive Valence Systems (PVS) domain. Associated paradigms examine how differences in outcome probability (first order uncertainty) and action-outcome contingency uncertainty (second order uncertainty) impact choice between alternate options. Within the Negative Valence Systems (NVS) domain, the construct of potential threat (anxiety) does not include consideration of the impact of potential threat, its probability and action- outcome contingency uncertainty, upon action valuation or choice; in addition paradigms listed under the NVS domain have no choice (instrumental) element and use physiological indices as dependent measures. These differences between constructs and tasks across the PVS and NVS domains hinder attempts to elucidate whether psychopathology-related deficits in probabilistic decision-making and the factors influencing action valuation and choice are common across both domains or unique to one or the other. Here, we will address this by creating equivalent PVs (reward) and NVS (shock) versions of two probabilistic decision-making tasks. We will use a hierarchical Bayesian computational framework to model behavioral and brain (functional magnetic resonance imaging) data from PVS and NVS versions of each task. This data will be acquired from healthy adult humans with a range of anxiety and depressive symptomatology. In addition to group-level analyses, we will use bifactor analysis to examine the latent factors underlying variance in anxiety and depressive symptomatology across participants and will relate scores on these factors to parameter estimates obtained by modeling of behavioral and brain data. Using this approach, we will examine commonalities and differences in the mechanisms supporting probabilistic decision-making when potential outcomes are aversive versus rewarding and alterations to these mechanisms as a function of anxiety and depressive related symptomatology. We hope that this will advance our understanding of the aspects of decision-making disrupted in anxiety and depression and the potential consequences for daily life. An additional goal of this research is to provide tasks and models that can be used in future clinical studies of probabilistic decision-making across both PVS and NVS domains.