Safe and sensible 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, that is, 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 make four recommendations for safe and sensible baseline correction of pupil-size data: 1) use subtractive baseline correction; 2) visually compare your corrected and uncorrected data; 3) 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 4) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).

PyPlr: A versatile, integrated system of hardware and software for researching the human pupillary light reflex

“…There is more than one valid approach to the analysis of pupillometry data, but the optimal approach will depend on the type of experiment being run, the quality of the data, and the research question in mind. Kelbsch et al ( 2019 ) give an informative view on standards in pupillometry of the light reflex and many papers offer advice on best practices and specific issues to do with data analysis (e.g., Hayes & Petrov, 2015 ; Hershman et al, 2018 ; Kret & Sjak-Shie, 2019 ; Mathôt, 2017 ; Sirois & Brisson, 2014 ; Winn et al, 2018 ), much of which is embodied in community-developed packages that aim to streamline the processing and analysis of pupillometry data (e.g., Acland & Braver, 2014 ; Hershman et al, 2019 ; Mittner, 2020 ). Ultimately, data analysis is a personal choice, and researchers would do well to explore the options that are available.…”

PyPlr: A versatile, integrated system of hardware and software for researching the human pupillary light reflex

“…There is more than one valid approach to the analysis of pupillometry data, but the optimal approach will depend on the type of experiment being run, the quality of the data, and the research question in mind. Kelbsch et al (2019) give an informative view on standards in pupillometry of the light reflex and many papers offer advice on best practices and specific issues to do with data analysis (e.g., Hayes & Petrov, 2015;Kret & Sjak-Shie, 2019;Mathôt, 2017;Sirois & Brisson, 2014;Winn et al, 2018), much of which is embodied in community-developed packages that aim to streamline the processing and analysis of pupillometry data (e.g., Acland & Braver, 2014;Mittner, 2020).…”

Exploring the interaction between the pupillary light response after exogenous attentional cueing and detection performance

“…We extracted epochs from the pupil time series from -1.25 ms before, to 2.06 ms after cue onset. All pupil sizes were normalized by subtracting the average pupil size in the window of -0.2 to 0 ms before cue onset (Mathôt, Fabius, Heusden, & Stigchel, 2018).…”