Pupil size modulations have been used for decades as a window into the mind, and several pupillary features have been implicated in a variety of cognitive processes. Thus, a general challenge facing the field of pupillometry has been understanding which pupil features should be most relevant for explaining behavior in a given task domain. In the present study, a longitudinal design was employed where participants completed 8 biweekly sessions of a classic mental arithmetic task for the purposes of teasing apart the relationships between tonic/phasic pupil features (baseline, peak amplitude, peak latency) and two task-related cognitive processes including mental processing load (indexed by math question difficulty) and decision making (indexed by response times). We used multi-level modeling to account for individual variation while identifying pupil-to-behavior relationships at the single-trial and between-session levels. We show a dissociation between phasic and tonic features with peak amplitude and latency (but not baseline) driven by ongoing task-related processing, whereas baseline was driven by state-level effects that changed over a longer time period (i.e. weeks). Finally, we report a dissociation between peak amplitude and latency whereby amplitude reflected surprise and processing load, and latency reflected decision making times.
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