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

项目摘要 在我们的日常生活中，我们选择不同的行动方针，希望达到预期的积极效果。 结果和避免担心的负面结果;这是复杂的各种形式的不确定性，影响 任何给定行为导致特定结果的概率。在NIMH的RDoC框架内，研究 基于奖励的行动评价和选择所涉及的机制中， PVS（Positive Valence Systems）相关范例检查结果的差异 概率（一阶不确定性）和行动-结果偶然性不确定性（二阶不确定性） 影响备选方案之间的选择。在负价系统（NVS）域中， 潜在威胁（焦虑）不包括对潜在威胁的影响、其可能性和行动的考虑- 基于行动评估或选择的结果偶然性不确定性;此外，NVS下列出的范例 域没有选择（工具）元素，并使用生理指标作为依赖的措施。这些 在PVS和NVS域之间的结构和任务之间的差异阻碍了阐明 是否与精神病理学相关的概率决策缺陷和影响行动的因素 评价和选择在这两个领域都是共同的，或者对一个或另一个是独特的。在这里，我们将解决这个问题， 通过创建两个概率决策任务的等价PV（奖励）和NVS（电击）版本。我们 我将使用分层贝叶斯计算框架来模拟行为和大脑（功能磁 共振成像）数据。该数据将从健康成人中获取 患有一系列焦虑和抑郁症的人。除了组级分析外，我们还将使用 双因素分析，以检查潜在的因素，在焦虑和抑郁症的变异 并将这些因素的得分与通过建模获得的参数估计值相关联， 行为和大脑数据。使用这种方法，我们将研究 当潜在结果是令人厌恶的还是值得的时，支持概率决策的机制 以及这些机制的改变作为焦虑和抑郁相关神经病学的函数。我们希望 这将促进我们对焦虑和抑郁中决策中断方面的理解 以及对日常生活的潜在影响。本研究的另一个目标是提供任务和模型 这可以用于未来的临床研究的概率决策跨PVS和NVS域。

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.