Safe and sensible preprocessing and baseline correction of pupil-size data

Abstract

Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, i.e., analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size/baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size − baseline). We discuss the role of baseline correction as a part of preprocessing of pupillometric data, and make five recommendations: (1) before baseline correction, perform data preprocessing to mark missing and invalid data, but assume that some distortions will remain in the data; (2) use subtractive baseline correction; (3) visually compare your corrected and uncorrected data; (4) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and (5) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

Visual Behavior, Pupil Dilation, and Ability to Identify Emotions From Facial Expressions After Stroke

“…A total of 111 individuals, 46 with stroke and 65 healthy controls, participated in the study. The group of individuals with stroke-either ischemic (n = 18) or hemorrhagic (n = 28)consisted of 23 women and 23 men with a median time since injury of 428.0 (222-678) days and a median age of 53.5 (44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58) years. The control group consisted of 35 women and 30 men, with a median age of 48 (30-79) years.…”

Pupil dilation is sensitive to semantic ambiguity and acoustic degradation

“…Data were low-pass filtered at 10 Hz (Kaiser window, length: 201 points). Single-trial time courses were baselinecorrected by subtracting the mean pupil size from the -0.5 s to 0 s time window from the pupil size value at each time point (Mathôt et al, 2018). Single-trial time courses were averaged separately for each condition, and displayed for the -0.5 s to 4 s epoch.…”

The human task-evoked pupillary response function is linear: Implications for baseline response scaling in pupillometry

“…Pupillometry lacks consensus and/or best practice guidelines for determining: (a) minimal thresholds that constitute meaningful differences between events, (b) binocular versus monocular pupil sampling, (c) temporal downsampling and smoothing procedures, (d) modeling TEPRs using diameter versus surface area of the pupil, and (e) objectively defining a peak range and/or dissociating peaks. Mathôt et al (2018) recently contributed a great service to the field by proposing a formal set of guidelines that will help standardize baseline correction.…”

“…Despite the relative maturity of pupillometry as a psychophysical measurement technique, the field has yet to empirically validate and/or reach methodological consensus on a definitive baseline correction procedure (but see Mathôt, Fabius, Van Heusden, & Van der Stigchel, 2018). 1 Nevertheless, an informal set of practices has emerged.…”