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Reliable quantification of patients’ cognitive arousal is a challenging problem and a pertinent clinical need in various mental health applications. Recently, skin conductance-based cognitive state estimation has shown promise in inferring the cognitive arousal of individuals caused by autonomic nervous system (ANS) activation. Here, we use a physiological model of ANS-stimulated skin conductance modulations and Bayesian filtering to analyze changes in cognitive arousal induced by auditory, visual, and haptic stimuli. Our findings indicate that cognitive arousal-based measures are in better agreement with self-ratings-based metrics than inferred autonomic nervous system activation events in response to sensory stimuli. These insights on cognitive arousal increase our understanding of psychophysiology and may help diagnose, track, and treat symptoms of mental health disorders in the future by providing clinicians with a framework to estimate and modulate arousal levels in an interactive sensory stimulation environment.