SummaryDuring sensory-guided decision-making, an animal’s behavior fluctuates between distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that guide transitions between these states. Because arousal is known to modulate neural dynamics and task performance on a moment-to-moment basis, we hypothesize that changes in arousal are related to performance states. Here, combining behavioral experiments in mice with computational modeling, we uncovered lawful relationships between transitions in strategic task performance states and an animal’s arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states. Optimal state epochs were predicted by intermediate levels, and reduced variability, of pupil diameter, along with reduced variability in face movements and locomotion. Our results demonstrate that arousal measures can predict optimal performance states, and suggest mice regulate their arousal during optimal performance.