Collaborative Research: CRCNS Research Proposal: Adaptive Decision Rules in Dynamic Environments

当做出决定时--既可以是在哪里吃午饭的简单决定，也可以是追求什么职业的复杂决定--通常会花时间考虑不确定的证据和可能的结果。这种深思熟虑的过程是行为和认知灵活性的核心，但需要克服一个重大挑战：决定何时停止深思熟虑，并致力于一项行动方针。许多被用来研究人类决策和实现机器决策的模型都假设决策承诺发生在累积的证据达到固定值或界限时。这些“积累到约束”模型在上个世纪的发展帮助形式化和改进了与代码破译、制造和其他真实世界应用相关的决策，也为理解大脑中的决策承诺提供了一个有用的起点。然而，固定边界在固定条件下最合适，固定条件经常用于实验室实验，但在现实世界中很少遇到。这项研究的目标是超越“固定界限”的决策承诺形式，而是考虑大脑用来结束审议并做出决定的更灵活的方式。这项研究从一个新的、以数学为基础的理论开始，该理论描述了在不断变化的条件下使用灵活的承诺形式的优势。该项目的目标是使用这一理论来设计和解释实验，这些实验将为人类大脑如何做出决策提供一个全面的新视角，即使这些决策是基于在不确定和不断变化的条件下进行的审议。这项工作将为来自不同背景的本科生和研究生提供数学、认知科学、心理学和神经科学相结合的跨学科培训。该团队将利用与研究相关的活动来鼓励未被充分代表的群体参与科学。此外，该团队将开发和传播资源，包括新的数据集和分析工具，这将有助于大脑如何做出决策的研究和教育。最后，该团队将提高公众对计算神经科学的认识，并解决提高科学素养和理解科学如何造福社会的迫切需要。这将通过公开讲座、为全国公共广播电台播出的“我们的独创性引擎”节目、大脑意识周活动以及向一个向非专业人士解释大脑研究的网站捐款来实现。深思熟虑的决定将大脑从条件反射处理的直接性中解放出来，但构成了一个关键的挑战：大脑如何决定何时停止深思熟虑，并致力于一项行动？我们对这一承诺过程的理解一直被一种计算框架所主导，该框架假设一旦积累的证据达到预定义的水平或界限，决定就会终止。这些“累积到约束”的模型与规范理论有着密切的联系，可以解释一系列的行为和神经发现。然而，它们只有在许多决策研究中使用的高度限制性条件下才是最优的，在这些条件下，证据的信息量、获取速度和其他特征是稳定的和预先知道的。当暂时延长的决策必须与我们动态和不确定的世界相抗衡时，我们还不知道大脑如何更广泛地平衡决策审议和承诺。我们的目标是促进我们对大脑在这些条件下使用的决策规则的理解。PI从一种新颖的理论基础开始，它包括灵活的决策界限，这些界限不是预定义的，而是可以在决策形成时进行更新，以优化性能，即使在不断变化的条件下也是如此。他们使用这个框架来指导人类行为研究的设计和分析，并结合非人类灵长类动物的行为和神经生理学研究，他们使用这些研究来验证他们的基本假设，即灵长类动物的大脑使用动态的、自适应的规则来支持在变化和不确定的环境中做出理性决策。该项目由数学和物理科学局(MPS)内的数学科学(DMS)司、计算机和信息科学与工程局(CEISE)内的信息和智能系统(IIS)司以及社会、行为和认知科学(BCS)局内的行为和认知科学(BCS)司共同资助。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

When decisions are made, which can be as simple as where to have lunch or as complicated as what career to pursue, time is often spent deliberating over uncertain evidence and possible outcomes. This deliberation process is central to behavioral and cognitive flexibility but requires overcoming a major challenge: determining when to stop deliberating and commit to a course of action. Many models that have been used to study human decision-making and to implement machine decision-making assume that decision commitment occurs when the accumulated evidence reaches a fixed value or bound. These “accumulate-to-bound” models, whose development last century helped formalize and improve decisions related to codebreaking, manufacturing, and other real-world applications, have also provided a useful starting point for understanding decision commitment in the brain. However, fixed bounds are most appropriate under fixed conditions, which are often used in laboratory experiments but rarely encountered in the real world. The goal of this study is to move beyond the “fixed bound” form of decision commitment and instead consider more flexible ways the brain uses to end deliberating and arrive at a decision. The research starts with a new, mathematically grounded theory that describes the advantages of using flexible forms of commitment under changing conditions. The goal of the project is to use this theory to design and interpret experiments that will provide a comprehensive new view of how human brains commit to decisions even when they are based on deliberations that occur during uncertain and changing conditions. This work will provide interdisciplinary training at the interface of mathematics, cognitive science, psychology, and neuroscience for undergraduate and graduate students from diverse backgrounds. The team will use research-related activities to encourage the participation of underrepresented groups in science. Also, the team will develop and disseminate resources, including novel datasets and analytic tools, that will benefit research and education in how the brain makes decisions. Finally, the team will increase public awareness of computational neuroscience and address the urgent need to increase scientific literacy and understanding of how science can benefit society. This will be accomplished via public lectures, contributions to the program “Engines of Our Ingenuity” broadcast by National Public Radio stations nationwide, Brain Awareness Week activities, and contributions to a website that explains brain research to non-specialists. Deliberative decisions free the brain from the immediacy of reflexive processing but pose a critical challenge: how does the brain decide when to stop deliberating and commit to a course of action? Our understanding of this commitment process has been dominated by a computational framework that assumes decisions are terminated once accumulated evidence reaches a predefined level or bound. These “accumulate-to-bound” models have close ties to normative theory and can explain a range of behavioral and neural findings. However, they are optimal only under the highly restrictive conditions used in many decision studies, in which the informativeness, rate of acquisition, and other features of the evidence are stable and known in advance. It is not known how the brain balances decision deliberation and commitment more generally, when temporally extended decisions must contend with our dynamic and uncertain world. The goal is to advance our understanding of the decision rules used by the brain under these conditions. The PIs start with a novel theoretical foundation that includes flexible decision bounds that are not predefined but instead can be updated while the decision is being formed to optimize performance even under changing conditions. They use this framework to guide the design and analysis of behavioral studies in humans and combine behavioral and neurophysiological studies in non-human primates, which they use to test their primary hypothesis that the primate brain uses dynamic, adaptive rules to support rational decision-making in changing and uncertain environments.This project is co-funded by the Division of Mathematical Sciences (DMS) within the Mathematical and Physical Sciences Directorate (MPS), Division of Information and Intelligent Systems (IIS) in the Directorate of Computer and Information Science and Engineering (CISE), and Division of Behavioral and Cognitive Sciences (BCS) within the Directorate for Social, Behavioral, and Economic Sciences (SBE).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.