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.

为了适应性地行事，人们需要灵活地将他们的决策与他们的短期和长期目标相结合。这种以目标为导向的决策失败会导致一系列适应不良的行为，如金融风险、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何时以及为什么他们不能这样做的理解仍然存在重大差距。在一系列将联合收割机计算建模与行为、注意力集中和大脑活动测量相结合的研究中，我们正在研究人类决策者灵活调整其信息搜索和行动以适应不同目标和任务需求的能力和局限性。通过揭示目标导向行为的隐藏杠杆和潜在失败模式，我们的工作更好地解开了现实世界决策失败的根源，并提供了一条实现有针对性干预的路径，以更好地使选择与长期目标保持一致。我们的项目解决了神经和计算机制研究中的关键空白，这些机制将决策和认知控制联系起来。过去关于目标导向决策的计算和神经机制的研究一直集中在人类目标的一个狭窄子集上（人们如何从一组目标中选择最佳选项）。因此，我们不知道人们如何灵活地重新配置自己的目标--例如，根据不同的标准进行选择，或者在不同的选项之间积累信息，而不是进行比较--以及他们何时以及为什么没有这样做。我们的项目开发和测试一个计算框架，适应在人类决策中观察到的灵活性范围，通过代表明确的选择目标，定义a）信息如何转化为证据和B）如何将证据整合到选择响应。我们正在一系列研究中测试我们的框架，这些研究将联合收割机行为任务与眼动追踪、脑电图和功能磁共振成像相结合。这种多模态方法使我们能够揭示神经回路和动力学，使人们能够灵活地转换和整合有关其选项的信息，以实现其当前目标，并了解人们如何以及为什么在这些能力方面有所不同。该项目进一步支持旨在培训研究人员预测和测试各种决策行为的计算方法的推广活动。一个配套项目正在由德国联邦教育和研究部（BMBF）资助。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。