Ahn Woo-Young, Haines Nathaniel, Zhang Lei
Department of Psychology, The Ohio State University, Columbus, OH.
Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Comput Psychiatr. 2017 Oct 1;1:24-57. doi: 10.1162/CPSY_a_00002. eCollection 2017 Oct.
Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.
强化学习与决策制定（RLDM）提供了一个定量框架和计算理论，借助它们我们可以将精神疾病分解为神经认知功能的基本维度。RLDM为评估和潜在诊断精神科患者提供了一种新方法，临床研究人员对RLDM和计算精神病学的热情与日俱增。这样一个框架还可以为特定RLDM过程的脑基质提供见解，功能性磁共振成像（fMRI）或脑电图（EEG）数据的基于模型分析就是例证。然而，研究人员常常觉得这种方法过于技术化，难以将其应用于自己的研究。因此，迫切需要开发一种用户友好型工具，以广泛传播计算精神病学方法。我们介绍一个名为hBayesDM（决策任务的分层贝叶斯建模）的R包，它提供了一系列RLDM任务和社会交换博弈的计算建模。hBayesDM包提供了最先进的分层贝叶斯建模，其中个体和组参数（即后验分布）以相互约束的方式同时进行估计。同时，该包极其用户友好：用户只需一行代码就能进行计算建模、输出可视化以及贝叶斯模型比较。用户还可以提取基于模型的fMRI/EEG所需的逐试验潜在变量（例如预测误差）。借助hBayesDM包，我们预计任何对编程稍有了解的人都能利用前沿的计算建模方法来研究多个决策制定（例如目标导向、习惯和巴甫洛夫式）系统之间的潜在过程和相互作用。通过这种方式，我们期望hBayesDM包将有助于先进建模方法的传播，并使众多研究人员能够轻松地在不同人群中开展计算精神病学研究。
