Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis

Mathôt, Sebastiaan; Vilotijević, Ana · 2022 · Crossref

DOI: 10.3758/s13428-022-01957-7

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Summary

This paper addresses the lack of standardized methodology in cognitive pupillometry, the measurement of pupil size to investigate cognitive processes such as attention, mental effort, and working memory. The authors note that experimental approaches vary widely across research groups, complicating replication and interpretation. To resolve this, the paper provides a comprehensive, hands-on guide for conducting trial-based cognitive-pupillometry experiments, focusing on experimental design, data preprocessing, and statistical analysis. The authors illustrate their proposed workflow using a specific example experiment investigating whether pupil size increases as a function of attentional breadth (attending to visual periphery vs. central vision). The methodological guidelines emphasize strict control of confounding variables. The authors advocate for the "Hillyard principle," where visual stimuli should be constant between conditions except for the cognitive manipulation, to avoid low-level visual properties (e.g., brightness, contrast, color) affecting pupil size. Eye position should ideally remain constant to prevent artifacts like the pupil-foreshortening error or genuine pupil changes due to eye movement effort. Experiments should be slow-paced, with inter-trial intervals of at least 3 seconds to minimize carryover effects, and pupil size should be measured during passive viewing periods rather than during motor responses to avoid response-locked dilation. Ambient lighting should be intermediate (e.g., 33 lux) and matched to display luminance to prevent discomfort glare and ensure pupils are not at physiological limits. Data collection should log all experimental variables and timestamps into a single file per participant to facilitate analysis. The preprocessing section outlines a pipeline for transforming raw eye-tracking data into an analysis-friendly structure. This involves parsing raw files into a format where time-series data (pupil size) and single-value data (response times) coexist. The authors detail steps for handling missing data (e.g., due to blinks) and invalid data (e.g., poor signal quality), recommending the identification and removal of invalid points before interpolation or smoothing. The paper provides Python code and toolboxes to implement these steps, demonstrating how to extract task-evoked pupil responses from the example experiment. The significance of this work lies in its provision of a concrete, reproducible workflow for cognitive pupillometry. By offering specific guidelines and open-source code, the authors aim to standardize practices in the field, improving the reliability and comparability of pupillometry research. The paper serves as a practical resource for researchers, bridging the gap between conceptual guidelines and implementation, and ensuring that experimental designs adequately control for the numerous factors that influence pupil size.

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• workload measurement

Information type

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• Empirical Findings: physiological data
• Methodological Resource: measurement protocol, tool software