How animals generate effective behavior in new situations is one of the longstanding mysteries of behavioral science. For example, every time a tennis player returns a serve, conditions are different from those the player has experienced in the past: the ball is travelling at a slightly different speed, or at a different angle, or with a different rotation. Yet, the nervous system is able to generate a response that is effective under these novel conditions. How is this possible? This project will address this question by combining new mathematical methods with a novel experimental system that uses real-time computer vision to track animals as they solve sequential decision tasks. As part of the broader impacts of the study, the investigators will partner with an undergraduate education program intended to introduce research opportunities to traditionally underrepresented STEM students. Interdisciplinary teams of students will be mentored in developing and executing small research projects related to the theme of the larger project with the goal providing a gateway for both basic science majors, and also engineering and mathematics majors into cutting-edge, interdisciplinary science. In addition, the investigators will incorporate this research when mentoring participants in the University of Florida's longstanding Whitney Lab REU program (31 years). The results of this work will shed light on how animals generate such a diverse range of behaviors to survive in novel situations, and will lead to new questions and approaches that can be used to understand the function of the vertebrate nervous system and the origins of behavioral control in some of the most important tasks animals undertake.
Unlike binary choice tasks, which have long served as the model for animal decision-making, more complex sensory-motor behaviors such as avoiding predators or capturing prey often involve sequences of decisions made in response to dynamic streams of sensory stimuli. A key requirement of such behaviors is that they be robust. For example, the exact chain of decisions required to escape a predator will differ from one setting to another, yet an animal must generate a sequence of responses uniquely suited to the situation at hand. This highlights a perennial question about animal behavior: how can behaviors that are learned or evolved in one context generalize to the enormous set of possible situations an animal might encounter? This project attacks this question using a combination of mathematical modeling and high-resolution, closed loop experiments using the prey attack and predator evasion behaviors of rainbow trout (Oncorhynchus mykiss) as a model for studying how animals generate flexible, robust behavioral sequences. In particular, the investigators will test the emerging hypothesis that these robust, higher-level behavioral responses are achieved through a mechanism called behavioral gain control. Research will explore how behavioral gain control is involved in initiating behavioral sequences at the right time, balancing multiple competing objectives, coordinating control along multiple behavioral dimensions (e.g., acceleration, turning), and maintaining performance across changing environmental conditions. This work has the potential to shed new light on how complex behaviors are generated in the context of ecologically and evolutionarily relevant tasks.
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.
