From pre-processing to advanced dynamic modeling of pupil data

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we provide recommendations and offer an up-to-date account of how pupil data can be analyzed in hypothesis-testing experiments. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements, visual features (e.g., luminance), and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. We discuss pre-processing steps and contend that the insights gained from pupillometry are constrained by the analysis techniques available. Then, in addition to the traditional approach of analyzing mean pupil size within some epoch of interest, we focus on time series-based analyses, which enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus series, task of interest, behavioral outcome measures, or other participants' pupil traces. Analytic techniques considered include: correlation (auto-, and cross-, reverse-, and inter/intra-subject-), regression (including temporal response functions), classification, dynamic time warping, phase clustering, magnitude squared coherence, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages.