Enhancing evaluation of reward learning using computational modeling methods

对行为任务的研究通常依赖于理解病因过程、相关性和 精神变态的后果。在奖励学习和决策的背景下，爱荷华州赌博 任务(IGT)是常用的。归根结底，研究通常依赖于任务的原始实施 允许参与者在任务中充分指导探索和学习。这降低了研究人员的能力 区分奖惩学习。此外，尽管IGT涉及多个进程 性能，任务通常被总结为单一的性能指标(即 有利/不利的刺激)。更现代的方法，包括计算建模， 提供区分过程的方法(例如，奖励学习、惩罚学习、奖励 敏感性、持之以恒的倾向)，对有关变更的实质性研究问题感兴趣 在精神病理学方面起作用的。该领域的一个关键限制是，使用计算的研究 使用IGT的建模方法只使用了任务的原始版本。更强的试验性 范型可以提高估计参数的精度。此外，计算建模研究 的IGT，特别是在发育样本中，依赖于排除检查的横断面设计 测试-重新测试可靠性或纵向变化。该项目将估计IGT的计算模型，使用 任务的更新版，在所有试验中对刺激呈现进行完全的实验控制。在……里面 独立样本(研究1 n=50名大学生；研究2[R01 MH107495]子女和父母(n= 248)，多次给予IGT(研究1两次，间隔约4周；研究2向上 到五次，子女和父母大约相隔9个月)。使用研究1的数据，我们将 开发奖励学习计算模型来描述IGT更新版中的任务行为。 使用研究2的数据，我们将检验模型对独立样本的泛化能力。我们会 使用计算建模方法检查性能的测试-重新测试可靠性，但扩展 重复评估的次数。我们还将检查模型参数相对于以下指标的有效性 奖赏功能测量(包括子女和父母的自我报告；子女的大脑功能； 和抑郁症家族史)。最后，我们还将调整计算模型以包括以下索引 开发以模拟后代样本中的变化。除了对国际货币基金组织 通过这些任务和模型的适应来评估奖励学习和决策，我们还将使用 我们通过更新开源软件(HBayesDM)来帮助传播这些模型 包)，以包括我们的模型，供其他调查人员使用。

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.