ArticleBehavior research methods2024
First steps into the pupillometry multiverse of developmental science.
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- The Influence of Hearing Aid Type on Reading: Results of an Eye-Tracking Study at University.Audiology research · 2026Article
- Reduced Pavlovian Value Updating Alters Decision-Making in Sign-Trackers.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Dimensionality reduction techniques in pupillometry research: A primer for behavioral scientists.Behavior research methods · 2025Article
- Altercentric Memory Error at 9 Months But Correct Object Memory by 18 Months Revealed in Infants' Pupil.Developmental science · 2025Article
- Manipulation of Intensive Longitudinal Data: A Tutorial in R With Applications on the Job Demand-Control Model.International journal of psychology : Journal international de psychologie · 2025Article
- No clear evidence for a domain-general violation of expectation effect in the pupillary responses of 9- to 10-month-olds.PloS one · 2025Article
- Pupillary manifolds: uncovering the latent geometrical structures behind phasic changes in pupil size.Scientific reports · 2024Article
- Infants' reorienting efficiency depends on parental autistic traits and predicts future socio-communicative behaviors.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Let's See If You Can Hear: The Effect of Stimulus Type and Intensity to Pupil Diameter Response in Infants and Adults.Ear and hearingArticle
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3 authors.
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Abstract
Pupillometry has been widely implemented to investigate cognitive functioning since infancy. Like most psychophysiological and behavioral measures, it implies hierarchical levels of arbitrariness in preprocessing before statistical data analysis. By means of an illustrative example, we checked the robustness of the results of a familiarization procedure that compared the impact of audiovisual and visual stimuli in 12-month-olds. We adopted a multiverse approach to pupillometry data analysis to explore the role of (1) the preprocessing phase, that is, handling of extreme values, selection of the areas of interest, management of blinks, baseline correction, participant inclusion/exclusion and (2) the modeling structure, that is, the incorporation of smoothers, fixed and random effects structure, in guiding the parameter estimation. The multiverse of analyses shows how the preprocessing steps influenced the regression results, and when visual stimuli plausibly predicted an increase of resource allocation compared with audiovisual stimuli. Importantly, smoothing time in statistical models increased the plausibility of the results compared to those nested models that do not weigh the impact of time. Finally, we share theoretical and methodological tools to move the first steps into (rather than being afraid of) the inherent uncertainty of infant pupillometry.
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