CAREER: Computing rules of the social brain: behavioral mechanisms of function and dysfunction in biological collectives

Biological collectives – groups of interacting biological individuals – are found at every level of organization: from the groups of individual cells that make up biological tissue, to the persistent social groups that characterize societies of higher animals. A hallmark of such collectives is coordination among individuals, which is critical to processes as diverse as pathogen clearance by populations of immune cells, coordinated development of a tissue, and swift but accurate decision-making by animal groups.

However, coordination is not the only behavior collectives exhibit. They can also, at times, be dominated by disorder and systemic dysfunction. Examples include the hyperactive immune responses behind autoimmunity and uncontrolled misinformation cascades in animal groups.

The goal of this project is to answer a question that has eluded scientists for decades: how and why do biological collectives coordinate seamlessly in some situations, yet exhibit catastrophic dysfunction in others? The project will answer this question by combining elegant experiments with mathematical and computational models that seek to identify the factors contributing to this variation.

This project will contribute to the empirical and theoretical foundations for solving diverse problems ranging from the control of distributed robots and sensor networks to health interventions aimed at promoting wound healing or disrupting collective behaviors of pathogens. Knowledge of the mechanisms behind collective dysfunction may also provide insights into topics as diverse as the conservation of animal migrations, management of population and ecosystem dynamics, and the neural mechanisms that underlie functional and dysfunctional social behavior.

Education and broader impact activities will focus on strengthening mathematical and AI literacy among life science students at undergraduate and graduate levels and sharing scientific knowledge beyond academia by engaging with natural history educators, science curriculum developers, and teachers.

This project seeks to reveal core scientific principles that govern why biological collectives function effectively under some circumstances, yet exhibit catastrophic dysfunction under others. The project will involve a strong feedback between theory development and experimentation with schooling fish – an iconic model of biological collectives. Using novel closed-loop experimental systems and AI-driven computer vision tools, the project will study how collectives solve ecologically-relevant tasks (e.g., predator evasion, decision-making, foraging) within precisely-controlled laboratory environments.

The project seeks to achieve three aims. Aim 1 focuses on how individuals cope with conflicting demands that arise when making decisions within a group. Aim 2 focuses on a critical issue that arises when individuals learn from one another: how to cope with misinformation.

Aim 3 focuses on a widely-documented phenomenon known as behavioral lock-in, wherein individuals in a group over-rely on information from others to the point of becoming unresponsive to the environment. The project will advance scientific knowledge by (i) identifying general mechanisms of function and dysfunction in biological collectives, (ii) discovering mechanistic links between individual phenotype, collective phenotype, and fitness-relevant outcomes, (iii) generating quantitative hypotheses about the neural basis of collective behavior, and (iv) developing general theoretical models of collective behavior that are strongly rooted in data.

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.