US-German Collaboration: Computational and Neural Mechanisms of Inference over Decision-Structure

美德合作：决策结构推理的计算和神经机制[j]；在这个项目中，主要研究人员将确定大脑如何能够识别适用于给定决策问题的相关规则，以便有效地做出决策。在大多数情况下，决策结构的特征是隐藏变量，即它们只能通过对结果变量（如奖励反馈）的离散观察来推断。因此，理解在嘈杂和部分可观察的环境中如何对决策结构进行推断是人类决策的计算神经生物学中一个基本但几乎未解决的问题。在这里，我们将决策问题的推理视为一种层次推理的形式，其中层次结构的较高层次表示当前决策结构的概率信念，而层次结构的较低层次编码当前在特定决策结构中奖励哪些行为的信念。我们将比较和对比部署不同策略的各种计算模型来解决这个问题。我们将这些模型与来自人类参与者的行为和功能磁共振成像（fMRI）数据相结合，以解决大脑中是否存在与此类分层模型的实现相关的动态信号，以及不同的大脑区域是否参与在不同层次上执行推理。这个项目可能会对前额皮质和其他大脑区域在决策中的作用有一个新的认识。该项目还将提供关于人类决策的神经生物学的基本缺失部分的神经实现的见解：决策结构推理。就更广泛的影响而言，这项研究可以为理解人类学习或决策失败或崩溃的情况提供基本的新见解。有时候，糟糕的学习或决策可能是由于未能推断出管理决策问题的正确规则，而不是学习或决策本身的困难。这样的见解不仅会影响研究决策的学术领域，还可以用来开发新的方法来帮助个人和组织做出更好的决策（通过专注于提高推理而不是结构）。这一发现可以为开发具有自主、灵活和适应性决策的人工智能体提供相关数据。该建议也具有很高的潜在临床相关性：妄想信念的障碍，如精神分裂症和边缘型人格障碍，可能在一定程度上涉及对决策结构进行推理的困难，以排除不适当的（决策）结构，而不是更适当的结构。目前的研究可能会产生新的工具来研究这个问题在临床人群。此外，对教学和培训也有重大影响。PI和co-PI教授本科生和研究生水平的课程，并让本科生研究人员直接参与他们的研究项目。这里提出的工作可能会导致新软件的开发，使使用计算模型分析脑成像数据成为可能。德国教育和研究部（BMBF）正在资助一个伙伴项目。

US-German Collaboration: Computational and Neural Mechanisms of Inference over Decision-StructurePI: John O?Doherty, Co PI: Peter BossaertsABSTRACTIn this project the Principal Investigators will determine how the brain is able to identify the relevant rules that apply to a given decision-making problem in order to effectively make decisions. In most cases, features of the decision structure are hidden variables, i.e. they can be inferred only through discrete observations of outcome variables (such as reward feedback). Understanding how inferences over decision-structure are performed in a noisy and partially observable environment is therefore a fundamental yet almost unaddressed issue in the computational neurobiology of human decision-making. Here, we conceive of inferences over decision problems as a form of hierarchical inference in which the higher level of the hierarchy represents probabilistic beliefs over which decision structure is currently in place, while the lower level of the hierarchy encodes beliefs over which actions are currently rewarded within a specific decision structure. We will compare and contrast a variety of computational models deploying different strategies to solve this problem. We will combine these models with behavioral and functional magnetic resonance imaging (fMRI) data from human participants in order to address whether dynamic signals are present in the brain pertaining to the implementation of such hierarchical models, and whether different brain regions are involved in performing inference at different levels of the hierarchy. This project could potentially lead to a new understanding of the contribution of the prefrontal cortex and other brain regions in decision-making. This project will also provide insight into the neural implementation of a fundamental missing part of the picture concerning the neurobiology of human decision-making: decision-structure inference.In terms of broader impacts, this research could provide fundamental new insights into understanding situations where human learning or decision-making fails or breaks down. Sometimes poor learning or decision-making may be due to a failure to infer the correct rules governing a decision-problem rather than a difficulty in learning or deciding per se. Such insights will not only impact on academic fields studying decision making but could also be used to develop novel methods to help individuals and organizations make better decisions (by focusing on improving inference over structure). The findings could provide relevant data for the development of artificial agents capable of autonomous, flexible and adaptive decisions. The proposal also has high potential clinical relevance: disorders with delusional beliefs such as schizophrenia and borderline personality disorder might involve in part a difficulty in performing inference over decision structure so as to rule out inappropriate (decision) structures in lieu of more appropriate ones. The present research might yield novel tools to study this question in clinical populations. Furthermore, there are substantial impacts on teaching and training. The PI and co-PI teach courses at undergraduate and graduate level and involve undergraduate researchers directly in their research programs. The work proposed here could lead to the development of new software to enable the analysis of brain imaging data using computational models. A companion project is being funded by the German Ministry of Education and Research (BMBF).