Abstract
Pupillometry is frequently used to examine cognitive aspects. While the experimental issues should be addressed during the measuring itself, there are several aspects that should be taken into account during the processing and analysis of the observed data. In the present chapter, we will present the most important aspects that appear during the preprocessing of the data (such as eyeblinks and eye movements), dealing with outliers, data normalization, baseline correction, data analysis, data presentation, manipulation check, and statistical methods. The chapter also includes hands-on practice for most of the discussed aspects. This chapter is written with the aim of supporting anyone interested in conducting a pupillometry study, regardless of its specific domain. We hope that at present, the guide proposed above is as up-to-date as possible and may assist in expanding the research circle of those conducting pupillometry studies.
We wish to thank Ms. Desiree Meloul for her helpful comments and useful input on this article.
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Notes
- 1.
The LC-NE system is one of several brainstem neuromodulatory nuclei, situated in the dorsal pons, with widely distributed, ascending projections to the neocortex, and is the sole source of cortical norepinephrine (NE).
- 2.
flip function returns the same array, but with the order of the elements reversed.
- 3.
CHAP calculates peak and dip parameters by using the mean pupil size curve. Users who are interested in analysis of these parameters by using trial-by-trial values may use the processed data created for each participant during the group analysis (trials_data.csv).
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This research was supported by the Israel Science Foundation grant No. 359/22 to Avishai Henik.
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Hershman, R., Milshtein, D., Henik, A. (2024). Processing and Analyzing of Pupillometry Data. In: Papesh, M.H., Goldinger, S.D. (eds) Modern Pupillometry. Springer, Cham. https://doi.org/10.1007/978-3-031-54896-3_15
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