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

美国-德国合作：决策结构推理的计算和神经机制PI：John O？Doherty，主要负责人：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的离散观察（如奖励反馈）来推断。因此，理解在嘈杂和部分可观察的环境中如何进行决策结构的推理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，我们设想的推理决策问题作为一种形式的分层推理，其中较高级别的层次结构表示概率信念的决策结构是目前到位，而较低级别的层次结构编码的信念，目前奖励的行动在一个特定的决策结构。我们将比较和对比各种计算模型部署不同的策略来解决这个问题。我们将联合收割机这些模型与行为和功能磁共振成像（fMRI）数据从人类参与者，以解决动态信号是否存在于大脑中有关的实施这种分层模型，以及是否不同的大脑区域参与执行推理的不同层次。该项目可能会导致对前额叶皮层和其他大脑区域在决策中的作用的新理解。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失败的情况提供基本的新见解。有时，学习或决策能力差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。这些见解不仅会对研究决策的学术领域产生影响，而且还可以用于开发新的方法，帮助个人和组织做出更好的决策（通过专注于改善结构推理）。研究结果可以为开发具有自主、灵活和自适应决策能力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：具有妄想信念的疾病，如精神分裂症和边缘型人格障碍，可能部分涉及对决策结构进行推理的困难，以便排除不适当的（决策）结构，而不是更合适的结构。目前的研究可能会产生新的工具，在临床人群中研究这个问题。此外，对教学和培训也有重大影响。PI和co-PI教授本科和研究生课程，并直接让本科研究人员参与他们的研究计划。这里提出的工作可能会导致新软件的开发，使大脑成像数据的分析使用计算模型。德国教育和研究部（BMBF）正在资助一个配套项目。

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).