A dynamical systems model of arousal-driven behavioural state transitions

When completing a task, animals switch between being engaged and disengaged. What neural and physiological processes trigger these behavioural state transitions? We estimated the engagement state of mice performing a visual decision-making task using a hidden Markov model of response times and found that intermediate arousal, as measured using baseline pupil size, was associated with more engagement in the task. Additionally, we show that changes in arousal (both mean and variability of the pupil baseline) predict changes in behavioural state. To explain this, we propose a double-well model, in which arousal causes behavioural state transitions by reshaping the attractor landscape of population neural activity. These results highlight a possible mechanism of arousal-related changes in behaviour.