The contribution of temporal analysis of pupillometry measurements to cognitive research

Hershman, Ronen; Milshtein, Dalit; Henik, Avishai

doi:10.1007/s00426-022-01656-0

Cited by 22 publications

(13 citation statements)

References 65 publications

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“…Next, for testing our qualitative hypothesis, we examined the temporal differences 44 between the adults and the children using Hershman and Henik's 37 approach. Specifically, we ran a series of Bayesian independent sample t-tests between the groups.…”

Section: Resultsmentioning

confidence: 99%

“…This time window was defined on the basis of the typical time course of pupil-size changes (which are very different from standard cognitive-behavioral measures such as reaction time e.g., 42 , 43 ). In addition, taking into consideration a relatively slow return of pupil size to baseline after the main dilation 37 , 44 , 45 , the time window was extended accordingly.…”

Section: Methodsmentioning

confidence: 99%

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A pupillometric study of developmental and individual differences in cognitive effort in visual word recognition

Shechter

Hershman

2022

Sci Rep

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Throughout the history of modern psychology, the neural basis of cognitive performance, and particularly its efficiency, has been assumed to be an essential determinant of developmental and individual differences in a wide range of human behaviors. Here, we examine one aspect of cognitive efficiency—cognitive effort, using pupillometry to examine differences in word reading among adults (N = 34) and children (N = 34). The developmental analyses confirmed that children invested more effort in reading than adults, as indicated by larger and sustained pupillary responses. The within-age (individual difference) analyses comparing faster (N = 10) and slower (N = 10) performers revealed that in both age groups, the faster readers demonstrated accelerated pupillary responses compared to slower readers, although both groups invested a similar overall degree of cognitive effort. These findings have the potential to open up new avenues of research in the study of skill growth in word recognition and many other domains of skill learning.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A pupillometric study of developmental and individual differences in cognitive effort in visual word recognition

Shechter

Hershman

2022

Sci Rep

Self Cite

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“…For instance, it allows examining the evidence in favor of the null hypothesis. It also does not rely on p values or on the significance level, so multiple comparisons do not cause alpha inflation (Hershman et al, 2022). A default Cauchy prior width of r = .707 was used for effect size on the alternative hypothesis (Rouder et al, 2012).…”

Section: Pupil Size Datamentioning

confidence: 99%

Attention does not spread automatically along objects: Evidence from the pupillary light response.

Luzardo¹,

Einhäuser²,

Michl³

et al. 2023

Journal of Experimental Psychology: General

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Objects influence attention allocation; when a location within an object is cued, participants react faster to targets appearing in a different location within this object than on a different object. Despite consistent demonstrations of this object-based effect, there is no agreement regarding its underlying mechanisms. To test the most common hypothesis that attention spreads automatically along the cued object, we utilized a continuous, response-free measurement of attentional allocation that relies on the modulation of the pupillary light response. In Experiments 1 and 2, attentional spreading was not encouraged because the target appeared often (60%) at the cued location and considerably less often at other locations (20% within the same object and 20% on another object). In Experiment 3, spreading was encouraged because the target appeared equally often in one of the three possible locations within the cued object (cued end, middle, uncued end). In all experiments, we added gray-to-black and gray-to-white luminance gradients to the objects. By cueing the gray ends of the objects, we could track attention. If attention indeed spreads automatically along objects, then pupil size should be greater after the gray-to-dark object is cued because attention spreads toward darker areas of the object than when the gray-to-white object is cued, regardless of the target location probability. However, unequivocal evidence of attentional spreading was only found when spreading was encouraged. These findings do not support an automatic spreading of attention. Instead, they suggest that attentional spreading along the object is guided by cue-target contingencies. Public Significance StatementThis study used the pupillary light response to challenge the common assumption that object-based attention spreads automatically along the object's contours. Instead, our findings suggest that attentional spreading is guided by current goals.

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“…To mitigate the issue of multiple comparisons, some researchers, including ourselves, have used an additional criterion, such as that p < .05 should hold for at least 200 contiguous milliseconds (e.g. Mathôt et al, 2017; see also Hershman et al, 2022 for a similar approach using Bayes Factors). This improves the issue somewhat, but it is not a formal way to correct for multiple comparisons; therefore, we no longer recommend this.…”

Section: Multiple Comparisons In Analysis Of Pupil-size Datamentioning

confidence: 99%

Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis

Mathôt

Vilotijević

2022

Preprint

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Cognitive pupillometry is the measurement of pupil size to investigate cognitive processes such as attention, mental effort, working memory, and many others. Currently, there is no commonly agreed-upon methodology for conducting cognitive-pupillometry experiments, and approaches vary widely between research groups and even between different experiments from the same group. This lack of consensus makes it difficult to know which factors to consider when conducting a cognitive-pupillometry experiment. Here we provide a comprehensive, hands-on guide to methods in cognitive pupillometry, with a focus on trial-based experiments in which the measure of interest is the task-evoked pupil response to a stimulus. We cover all methodological aspects of cognitive pupillometry: experimental design; preprocessing of pupil-size data; and statistical techniques to deal with multiple comparisons when testing pupil-size data. In addition, we provide code and toolboxes (in Python) for preprocessing and statistical analysis, and we illustrate all aspects of the proposed workflow through an example experiment and example scripts.

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The contribution of temporal analysis of pupillometry measurements to cognitive research

Cited by 22 publications

References 65 publications

A pupillometric study of developmental and individual differences in cognitive effort in visual word recognition

A pupillometric study of developmental and individual differences in cognitive effort in visual word recognition

Attention does not spread automatically along objects: Evidence from the pupillary light response.

Methods in Cognitive Pupillometry: Design, Preprocessing, and Statistical Analysis

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