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Related Experiment Video

Updated: Aug 30, 2025

Assessing Pupil-linked Changes in Locus Coeruleus-mediated Arousal Elicited by Trigeminal Stimulation

Assessing Pupil-linked Changes in Locus Coeruleus-mediated Arousal Elicited by Trigeminal Stimulation

Published on: November 26, 2019

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

Sebastiaan Mathôt¹, Ana Vilotijević²

¹Department of Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands. s.mathot@rug.nl.

Behavior Research Methods

|August 26, 2022

Summary

This summary is machine-generated.

This guide offers a standardized methodology for cognitive pupillometry, a technique measuring pupil size to study cognitive functions. It provides practical steps for experimental design, data preprocessing, and analysis, ensuring reliable results in cognitive research.

Keywords:

Cognitive neuroscience Cognitive psychology Data analysis Experimental design Pupillometry

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Last Updated: Aug 30, 2025

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Area of Science:

Cognitive Neuroscience
Psychophysiology
Experimental Psychology

Background:

Cognitive pupillometry measures pupil size to infer cognitive processes like attention and working memory.
Current methodologies for cognitive pupillometry lack standardization, leading to inconsistent research findings.
This variability complicates the design and interpretation of pupil-based cognitive experiments.

Purpose of the Study:

To provide a comprehensive, hands-on guide for conducting cognitive pupillometry experiments.
To establish a consensus on best practices for trial-based pupil response measurements.
To offer practical solutions for experimental design, data preprocessing, and statistical analysis.

Main Methods:

Detailed guidance on experimental design for cognitive pupillometry.
Standardized protocols for preprocessing task-evoked pupil size data.
Statistical techniques for handling multiple comparisons in pupil-size data analysis.

Main Results:

A complete workflow for cognitive pupillometry experiments is presented.
Open-source Python code and toolboxes are provided for data analysis.
An example experiment and scripts illustrate the proposed methodology.

Conclusions:

Standardized cognitive pupillometry methods enhance research reliability and reproducibility.
The provided guide and tools facilitate consistent application of pupillometry techniques.
This work aims to overcome current methodological challenges in cognitive pupillometry research.