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First steps into the pupillometry multiverse of developmental science.
Giulia Calignano1, Paolo Girardi2,3, Gianmarco Altoè2
1Department of Developmental and Social Psychology, University of Padua, Padua, Italy. giulia.calignano@unipd.it.
Behavior Research Methods
|July 13, 2023
Summary
This study reveals how data preprocessing choices significantly impact pupillometry results in infant cognitive research. Smoothing time in statistical models enhances result plausibility, offering tools to navigate analytical uncertainty.
Area of Science:
- Cognitive Neuroscience
- Developmental Psychology
- Psychophysiology
Background:
- Pupillometry is a key tool for studying infant cognitive development.
- Psychophysiological measures like pupillometry involve complex preprocessing steps.
- Understanding the impact of these steps is crucial for reliable infant research.
Purpose of the Study:
- To assess the robustness of pupillometry results using a multiverse analysis approach.
- To investigate how preprocessing choices and statistical modeling influence findings in infant studies.
- To compare the effects of audiovisual versus visual stimuli on infant attention.
Main Methods:
- A multiverse approach was used to analyze pupillometry data from 12-month-olds.
- Varied preprocessing steps (e.g., blink handling, artifact correction) and modeling structures were explored.
- A familiarization procedure compared responses to audiovisual and visual stimuli.
Main Results:
- Preprocessing decisions demonstrably influenced regression outcomes in infant pupillometry.
- Visual stimuli, compared to audiovisual, suggested a greater allocation of cognitive resources.
- Incorporating time smoothing in statistical models improved the plausibility of the findings.
Conclusions:
- The choice of data preprocessing and statistical modeling significantly affects pupillometry findings in infants.
- Time smoothing is a valuable technique for enhancing the reliability of infant pupillometry data.
- Methodological transparency and tools are needed to address the inherent uncertainties in infant pupillometry analysis.
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