CRCNS US-German Collaborative Research Proposal: Neural and computational mechanisms of flexible goal-directed decision making

CRCNS 美德合作研究提案：灵活目标导向决策的神经和计算机制

• 批准号: 2309022
• 负责人: Amitai Shenhav
• 金额: $ 81.22万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309022&HistoricalAwards=false
• 关键词: CRCNS; US; German; Collaborative; Research

To behave adaptively, people need to flexibly align their decisions with their immediate and long-term goals. Failures of such goal-directed decision-making can result in an array of maladaptive behaviors, such as financial risk taking, drug abuse, or gambling. While past research has helped identify the drivers of goal-directed decisions, there are still significant gaps in our understanding of how people are able to reorient those decisions as flexibly as they do, as well as when and why they fail to do so. In a series of studies that combine computational modeling with measures of behavior, attentional focus, and brain activity, we are studying the capacities and limits of human decision-makers to flexibly adjust their information search and actions to different goals and task demands. By uncovering the hidden levers and potential failure modes of goal-directed behavior, our work better disentangles sources of real-world decision-making failures, and provide a path towards targeted interventions to better align choices with long-term goals. Our project addresses critical gaps in research on the neural and computational mechanisms that link decision-making and cognitive control. Past work on the computational and neural mechanisms of goal-directed decision-making has been singularly focused on a narrow subset of human goals (how people select the best option from a set). It is therefore unknown how people flexibly reconfigure to their wider array of goals – for instance, selecting under different criteria or accumulating information across options rather than comparing between them – and when and why they fail to do so. Our project develops and tests a computational framework that accommodates the range of flexibility observed in human decision-making, by representing explicit choice goals that define a) how information is translated into evidence and b) how evidence is then integrated to select responses. We are testing our framework in a series of studies that combine behavioral tasks with eye-tracking, EEG and fMRI. This multi-modal approach allows us to uncover the neural circuits and dynamics that enable people to flexibly transform and integrate information about their options to achieve their current goals, and to understand how and why people vary in these capacities. The project further supports outreach activities aimed at training researchers in computational methods for predicting and testing a wide array of decision-making behavior.A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了做出适应性行为，人们需要灵活地将决策与近期和长期目标结合起来。如果这种以目标为导向的决策失败，可能会导致一系列适应不良行为，例如金融冒险、药物滥用或赌博。虽然过去的研究有助于确定目标导向决策的驱动因素，但我们对人们如何能够灵活地重新调整这些决策以及他们何时以及为何未能这样做的理解仍然存在重大差距。一系列的研究将计算模型与行为、注意力集中和大脑活动的测量相结合，我们正在研究人类决策者通过揭示隐藏的杠杆和潜力来灵活调整其信息搜索和行动以适应不同的目标和任务需求的能力和限制。目标导向行为的失败模式，我们的工作更好地理清了现实世界决策失败的根源，并提供了一条干预途径，以更好地将选择与长期目标结合起来。我们的项目解决了神经和神经系统研究中的有针对性的关键差距。连接决策和决策的计算机制过去关于目标导向决策的计算和神经机制的工作主要集中在人类目标的一小部分（人们如何从一组中选择最佳选项），因此人们如何灵活地重新配置以实现这一目标尚不清楚。他们更广泛的目标——例如，根据不同的标准进行选择或积累不同选项的信息，而不是在它们之间进行比较——以及他们何时以及为何未能做到这一点。人类决策，通过表达明确选择目标定义了a）如何将信息转化为证据，b）如何整合证据以选择反应。我们正在一系列研究中测试我们的框架，这些研究将行为任务与眼动追踪、脑电图和功能磁共振成像相结合。模态方法使我们能够揭示神经回路和动态，使人们能够灵活地转换和整合有关他们的选择的信息以实现当前的目标，并了解人们在这些能力上的差异和原因。该项目进一步支持旨在培训的外展活动。研究人员使用计算方法来预测和测试广泛的一系列决策行为。德国联邦教育和研究部 (BMBF) 正在资助一个配套项目。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持审查标准。