2022
DOI: 10.3758/s13428-022-01957-7
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Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis

Abstract: 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 pr… Show more

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Cited by 60 publications
(44 citation statements)
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“…We ran a four-fold cross-validation analysis on a predetermined time-window of 750-3000 ms after the onset of the cue to assess differences in pupil size across eccentricities. This procedure is described in more detail in Mathôt and Vilotijević (2022); however, in brief, this analysis determines a time point at which an effect of interest (Cue Eccentricity in our case) is strongest by splitting the data into training and test sets. Next, for each test set, the corresponding training set is used to determine the time point at which the effect is the strongest; a single linear mixed effects (LME) analysis is then conducted for the full dataset, using for each test set the time point that was selected based on the corresponding training set, thus avoiding both circularity and the need to correct for multiple comparisons.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…We ran a four-fold cross-validation analysis on a predetermined time-window of 750-3000 ms after the onset of the cue to assess differences in pupil size across eccentricities. This procedure is described in more detail in Mathôt and Vilotijević (2022); however, in brief, this analysis determines a time point at which an effect of interest (Cue Eccentricity in our case) is strongest by splitting the data into training and test sets. Next, for each test set, the corresponding training set is used to determine the time point at which the effect is the strongest; a single linear mixed effects (LME) analysis is then conducted for the full dataset, using for each test set the time point that was selected based on the corresponding training set, thus avoiding both circularity and the need to correct for multiple comparisons.…”
Section: Resultsmentioning
confidence: 99%
“…Following the workflow for preprocessing pupillary data that we described elsewhere (Mathôt & Vilotijević, 2022), we first interpolated blinks and downsampled the data by a factor of 10. Also, we converted pupil size measurements from arbitrary units to millimeters of diameter by using the formula specific to our lab (Wilschut & Mathôt, 2022).…”
Section: Methodsmentioning
confidence: 99%
“…Following the workflow for preprocessing pupillary data that we described elsewhere (Mathôt & Vilotijević, 2022), we first interpolated blinks and downsampled the data by a factor of 10. Also, we converted pupil size measurements from arbitrary units to millimeters of diameter by using the formula specific to our lab (Wilschut & Mathôt, 2022).…”
Section: Pupillary Data: Preprocessingmentioning
confidence: 99%
“…To investigate the effect of attentional breadth on pupil size, we ran a four-fold cross-validation analysis on a predetermined time-window of 750-3000 ms after the onset of the cue to assess differences in pupil size across eccentricities, after which a single linear mixed effects (LME) is conducted for the full dataset (this procedure is described in more detail in Mathôt & Vilotijević, 2022).…”
Section: The Effect Of Attentional Breadth On Pupil Sizementioning
confidence: 99%
“…A louder prestimulus leading noise will likely increase pupil size by mere virtue of sensation level (Liao et al 2016), and therefore the subsequent baseline-corrected values will be compressed. Matho ˆt et al (2022) warn against this practice in a thorough review of pupillometry experiment design. Despite concerns about an unwieldy interaction, several studies (Zekveld et al 2010(Zekveld et al 2011Koelewijn et al 2012;Parthasarathy et al 2020) presented speech-in-noise stimuli where the leading noise (rather than the speech) was varied in intensity to achieve target signal-to-noise ratios, where the data still produced interpretable results in line with the hypotheses.…”
Section: Time Scales Of Listening Effortmentioning
confidence: 99%