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Safe and sensible preprocessing and baseline correction of pupil-size data
Sebastiaan Mathôt1,2, Jasper Fabius3, Elle Van Heusden3
1Department of Psychology, Heymans Institute, University of Groningen, Groningen, The Netherlands. s.mathot@cogsci.nl.
Behavior Research Methods
|January 14, 2018
Summary
Pupil size analysis requires careful baseline correction to avoid data distortion. Subtractive correction is recommended over divisive methods, especially when dealing with potential artifacts like blinks in pupillometry data.
Area of Science:
- Psychology
- Cognitive Science
- Neuroscience
Background:
- Pupillometry, the measurement of pupil size, is increasingly used in psychological research.
- Standardized methods for analyzing pupillometry data, particularly baseline correction, are lacking.
Purpose of the Study:
- To evaluate the impact of baseline correction methods on pupillometry data.
- To provide recommendations for robust pupillometry data analysis.
Main Methods:
- Comparison of subtractive and divisive baseline correction techniques.
- Simulation of data distortions, such as those caused by blinks, during baseline periods.
Main Results:
- Divisive baseline correction is more susceptible to distortions from artifacts like blinks than subtractive correction.
- Unrealistically small baseline pupil sizes can significantly distort results.
Conclusions:
- Subtractive baseline correction is preferred for analyzing pupillometry data.
- Preprocessing and careful quality control, including removing trials with artifactual baseline pupil sizes, are crucial for accurate pupillometry analysis.
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