Building normative models of Reinforcement Learning Decision Making Behavior

建立强化学习决策行为的规范模型

• 批准号: 10572615
• 负责人: POORNIMA KUMAR
• 金额: $ 24.6万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10572615
• 关键词: Adopted; Age; Bayesian Method; Behavior; Behavior Disorders; Behavioral; Case/Control Studies; Clinical; Communities; Complex; Computer Models; Conflict (Psychology); Data; Data Reporting; Decision Making; Diagnosis; Diagnostic; Disease; Environment; Ethnic Origin; Failure; Family; Gender; Goals; Grain; Growth; Heterogeneity; Impairment; Individual; Learning; Link; Location; Longevity; Maps; Mathematics; Measures; Mental disorders; Modeling; Normal Range; Outcome; Participant; Patient Self-Report; Patients; Pediatrics; Population; Process; Psychiatry; Psychological reinforcement; Psychopathology; Research; Research Domain Criteria; Risk; Sample Size; Sampling; Shapes; Stimulus; Stratification; Structure; Symptoms; Testing; Time; Uncertainty; Update; aged; clinical phenotype; clinical practice; cohort; computer framework; cost; discounting; flexibility; improved; mathematical model; novel; precision medicine; psychiatric symptom; success; virtual

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.

精神病学领域的主要目标是能够在个体层面上治疗患者。实现这一目标 在精准医学领域，已经开发了诸如RDoC之类的大规模计划，以寻找新的方法来解析 精神疾病的异质性他们的成功一直缓慢，部分原因是从 基于诊断的病例对照研究，由于样本量小而存在局限性。因此，有一个关键的 我们需要以负担得起的成本找到替代解决方案。一种策略是识别一种复杂的行为， 基于强化学习的决策（RLDM）在各种精神疾病中受损 并采用计算框架来解释个体水平上的异质性。RLDM是一个多方面的 一个涉及多个子过程的结构，从估计环境中不同选项的价值 （估值），为这些选项积累证据（序贯抽样），选择最佳选项（探索- 利用行为），估计结果值（显著性归因），最后整合相关信息 关于结果和更新刺激值（学习率）。这些子过程可以通过以下方式量化： 利用计算机模型。然而，在建立这些RLDM结构的规范模型之前， 可能在临床实践中使用，评估这些RLDM模型衍生的可靠性至关重要。 参数以避免转换失败。因此，我们的主要目标是（1）解析RLDM子 使用一组不同的任务，在大样本中将过程转化为精确定义的参数;（2）评估 （3）建立各参数的规范化模型，绘制各参数的重测信度图， 个体水平的异质性。为了实现这一目标，我们将从六个RL获取行为数据， 任务，包括延迟和努力折扣，概率学习，虚拟补丁觅食，巴甫洛夫工具 转移和接近避免冲突的任务。这些任务每个都被优化以测量至少两个RL 子进程分开。我们将从社区样本中收集行为数据和自我报告评估 1000名参与者（18-85岁）。为了估计RLDM参数的重测信度，我们将邀请500名 健康的参与者在一周的时间内再次完成会议。首先，我们将开始- 最先进的计算模型来量化每个受试者的RLDM行为。第二，我们将计算重测 这些参数的可靠性。第三，我们将建立规范模型，将RLDM结构中的每一个与年龄联系起来 并计算出每个受试者与标准的偏差。最后，我们将对这些偏差进行软聚类 以确定集群，并调查他们在精神病理学和一般功能的差异。