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

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 Principal Investigators (PIs) then 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 PIs will use research-related activities to encourage the participation of underrepresented groups in science. Also, the PIs 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 PIs 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.