Department of Mechanical and Aerospace Engineering
Stability-Flexibility Dilemma in Cognitive Control: A Dynamical System Perspective
Constraints on control-dependent processing have become a fundamental concept in general theories of cognition that explain human behavior in terms of rational adaptations to these constraints. However, theories miss a rationale for why such constraints would exist in the first place. Recent work suggests that constraints on the allocation of control facilitate flexible task switching at the expense of the stability needed to support goal-directed behavior in face of distraction. We formulate this problem in a dynamical system, in which control signals are represented as attractors and in which constraints on control allocation limit the depth of these attractors. We derive formal expressions of the stability-flexibility tradeoff, showing that constraints on control allocation improve cognitive flexibility but impair cognitive stability. We provide evidence that human participants adapt higher constraints on the allocation of control as the demand for flexibility increases but that participants deviate from optimal constraints. In continuing work, we are investigating how collaborative performance of a group of individuals can benefit from individual differences defined in terms of balance between cognitive stability and flexibility.
This is joint work with Sebastian Musslick, Anastasia Bizyaeva, Shamay Agaron, and Jonathan Cohen and based on the paper: https://naomi.princeton.edu/wp-content/uploads/sites/744/2021/03/Musslick_et_al_CogSci2019.pdf
Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty in Applied and Computational Mathematics at Princeton University. She received her BSE in Mechanical Engineering from Princeton University and her PhD in Electrical Engineering from the University of Maryland. She is a MacArthur Fellow, Fellow of the American Academy of Arts and Sciences, SIAM, IEEE, IFAC, and ASME. Her current research focuses on dynamics and control of multi-agent systems on networks with application to distributed decision-making, spreading processes, collective behavior, and multi-robot teams.