A novel algorithm for decomposing pupil signals into tonic and phasic components outperforms traditional window-averaging methods in simulated and real-world datasets.
The human pupil is a widely used physiological metric in psychology and neuroscience. Changes in pupil diameter (PD) are thought to reflect changes in locus coeruleus-norepinephrine (LC/NE) activity, which is associated with cognitive and behavioral optimization. Here, we present a novel algorithm to decompose the pupil signal into its tonic and phasic components. We evaluate the utility and validity of the algorithms using both artificially generated data and an existing dataset from a fast-paced finger-tapping task. Results show that the novel algorithm outperforms traditional approaches on simulated data. We further demonstrate that our algorithm provides more conclusive evidence for relationships between mind wandering reports and pupil predictors compared to traditional window-averaging. Finally, we demonstrate that the novel and traditional estimates contain distinct information regarding neuroimaging correlates and task performance.
Mittner et al. (Fri,) studied this question.
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