Building normative models of Reinforcement Learning Decision Making Behavior

精神病学领域的主要目标是能够在个人层面上治疗患者。为了实现这一目标 在精确医学方面，已经开发了诸如RDoC这样的大规模倡议来寻找新的解析方法 精神障碍的异质性。他们的成功是缓慢的，部分原因是从 基于诊断和小样本限制的病例对照研究。因此，有一个关键的 需要以负担得起的成本找到替代解决方案。一种策略是识别复杂的行为，如 基于强化学习的决策(RLDM)在各种精神障碍中的损害 并采用计算框架来解释个体层面的异质性。RLDM是一个多方面的 构建涉及多个子过程，范围从估计环境中不同选项的值 (估值)，为这些期权积累证据(序贯抽样)，选择最佳选项(探索- 利用行为)，估计结果价值(显著归因)，最后整合相关信息 关于结果和更新刺激值(学习率)。这些子过程可以用以下公式来量化 利用计算模型。然而，在构建这些RLDM构造的标准化模型之前，可以 要潜在地用于临床实践，评估这些RLDM模型的可靠性至关重要 参数以避免转换失败。因此，本方案的主要目标是(1)解析RLDM子模型。 使用一组不同的任务在大样本中将过程转化为数学定义的参数；(2)评估 最后，(3)建立参数的规范模型，并绘制成图。 个体水平上的异质性。为了实现这一目标，我们将从六个RL获得行为数据 任务，包括延迟和努力折扣、概率学习、虚拟补丁搜寻、巴甫洛夫工具 转移和接近--避免冲突任务。这些任务中的每一个都被优化以测量至少两个RL 子进程分开进行。我们将从社区样本中收集行为数据和自我报告评估 来自1000名参与者(年龄为18-85岁)。为了评估RLDM参数的重测可靠性，我们将邀请500名 这个样本中的健康参与者在一周内再次完成治疗。首先，我们将应用Start- 最先进的计算模型，量化每个受试者的RLDM行为。其次，我们将计算重测 这些参数的可靠性。第三，我们将建立标准化模型，将每个RLDM结构与年龄联系起来 并计算每个受试者与常模的偏差。最后，我们将对这些偏差进行软聚类 识别集群并调查它们在精神病理学和一般功能方面的差异。

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.