Abstract
Reaction time (RT) is one of the most frequently used measures to detect cognitive processes. When tasks require more cognitive processes/resources, reaction is slower. However, RTs may provide only restricted information regarding the temporal characteristics of cognitive processes. Pupils respond reflexively to light but also to cognitive activation. The more cognitive resources a task requires, the more the pupil dilates. However, despite being able to use temporal changes in pupil size (advanced devices measure changes in pupil diameter with sampling rates of above 1000 samples per second), most past studies using pupil dilation have not investigated temporal changes in pupil response. In the current paper, we discuss the advantage of the temporal approach to analyze pupil changes compared to a more traditional perspective, specifically, singular value methods such as mean value and peak amplitude value. Using data from two recent studies conducted in our laboratory, we demonstrate the differences in findings arising from the various analyses. In particular, we focus on the advantage of temporal analysis in detecting hidden effects, investigating temporal characterizations of the effects, and validating the experimental manipulation.
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Notes
The frequency response of the pupil is typically less than 10 Hz, and most changes are far below that frequency (Klingner et al., 2011). Thus, including high sampling rates might produce more points for analysis that can adversely affect the statistical outcome and add noise to the overall signal.
In relatively simple cognitive tasks, information about the cognitive processes (at least the peak) generally appears at most 1,100 ms post-stimulus onset (Steinhauer & Hakerem, 1992). Hence, 2,000 ms is consider to be a reasonable time window.
Methods like GCA (that use orthogonal polynomials) and cluster-based permutation (that use cluster mass) have less of an issue of alpha infusion that occurs due to multiple comparisons. Actually, also when frequentist temporal methods are used, corrections such as false discovery rate (FDR; Einhauser et al., 2008) can hold the error rate constant.
The prior probability distribution that is used for the Bayesian analysis is the existing knowledge about the hypothesis before the evidence is taken into account.
Here the baseline was defined as the average pupil size 500 ms before script offset.
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We wish to thank Ms. Desiree Meloul for helpful comments and useful input on this article.
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This work was supported by the Israel Ministry of Sciences and Technology.
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Appendix
Appendix
Detailed Bayes Factor Analysis
See Figs.
Bayes factors (BFs) as a function of time for each comparison of Exp.1 from Hershman and Henik (2019). Each curve represents \(B{F}_{10}\) (namely, evidence for the alternative hypothesis that the two conditions are not the same) for the specific comparison. The horizontal black lines on 3 and on 0.3 represent the threshold for the decision making (\(B{F}_{10}\) values above 3 provide evidence for the alternative hypothesis and \(B{F}_{10}\) values below 1/3 provide evidence for the null hypothesis). Please note, the scale on the Y-axis is exponential
7 and
Bayes factors (BFs) as a function of time for the comparison between negative and positive scripts in the intentional group from Milshtein et al. (in preparation). The curve represents \(B{F}_{10}\) (namely, evidence for the alternative hypothesis that the two emotional conditions are not the same) for the specific comparison. The horizontal black lines at 3 and at 0.3 represent the threshold for the decision making (\(B{F}_{10}\) values above 3 provide evidence for the alternative hypothesis and \(B{F}_{10}\) values below 1/3 provide evidence for the null hypothesis). Please note, the scale on the Y-axis is exponential
8.
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Hershman, R., Milshtein, D. & Henik, A. The contribution of temporal analysis of pupillometry measurements to cognitive research. Psychological Research 87, 28–42 (2023). https://doi.org/10.1007/s00426-022-01656-0
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DOI: https://doi.org/10.1007/s00426-022-01656-0