A Primer on Design and Data Analysis for Cognitive Pupillometry

Reilly, Jamie; Zuckerman, Bonnie; Kelly, Alexandra

doi:10.31234/osf.io/j6sdt

Cited by 4 publications

(3 citation statements)

References 87 publications

(111 reference statements)

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“…More complex measures can also be derived from pupilsize data. For example, Reilly et al (2021) discuss how a pupil-dilation response can be quantified as a time-to-peak (the time it takes for the pupil to reach its maximum size), base-to-peak (the maximum size that the pupil reaches), and several other measures; Fink et al (2021) discuss how the synchronicity between pupil size and external events, such as rhythmic beats, can be quantified; and Wierda et al (2012) introduced a "deconvolution" technique to quantify how the (sluggish) pupil response is affected by individual events that are part of a rapid sequence of events, such as stimuli in a rapid-serial-visual-presentation (RSVP) stream. All of these approaches warrant a full discussion in their own right, and we therefore refer to these respective articles for further reading.…”

Section: Visualization and Statistical Analysismentioning

confidence: 99%

Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis

Mathôt

Vilotijević

2022

Behav Res

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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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Section: Visualization and Statistical Analysismentioning

confidence: 99%

Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis

Mathôt

Vilotijević

2022

Behav Res

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“…(For different statistical techniques to address different research questions see e.g. Fink et al, 2021; Reilly et al, 2021).…”

Section: Example Experimentsmentioning

confidence: 99%

“…More complex measures can also be derived from pupil-size data. For example, Reilly et al (2021) discuss how a pupil-dilation response can be quantified as a time-to-peak (the time it takes for the pupil to reach its maximum size), base-to-peak (the maximum size that the pupil reaches), and several other measures; Fink et al (2021) discuss how the synchronicity between pupil size and external events, such as rhythmic beats, can be quantified; and Wierda et al (2012) introduced a ‘deconvolution’ technique to quantify how the (sluggish) pupil response is affected by individual events that are part of a rapid sequence of events, such as stimuli in a rapid-serial-visual-presentation (RSVP) stream. All of these approaches warrant a full discussion in their own right, and we therefore refer to these respective articles for further reading.…”

Section: Visualization and Statistical Analysismentioning

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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Face Detection Using a Capsule Network for Driver Monitoring Application

et al. 2023

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Bus driver distraction and cognitive load lead to higher accident risk. Driver distraction sources and complex physical and psychological effects must be recognized and analyzed in real-world driving conditions to reduce risk and enhance overall road safety. The implementation of a camera-based system utilizing computer vision for face recognition emerges as a highly viable and effective driver monitoring approach applicable in public transport. Reliable, accurate, and unnoticeable software solutions need to be developed to reach the appropriate robustness of the system. The reliability of data recording depends mainly on external factors, such as vibration, camera lens contamination, lighting conditions, and other optical performance degradations. The current study introduces Capsule Networks (CapsNets) for image processing and face detection tasks. The authors’ goal is to create a fast and accurate system compared to state-of-the-art Neural Network (NN) algorithms. Based on the seven tests completed, the authors’ solution outperformed the other networks in terms of performance degradation in six out of seven cases. The results show that the applied capsule-based solution performs well, and the degradation in efficiency is noticeably smaller than for the presented convolutional neural networks when adversarial attack methods are used. From an application standpoint, ensuring the security and effectiveness of an image-based driver monitoring system relies heavily on the mitigation of disruptive occurrences, commonly referred to as “image distractions,” which represent attacks on the neural network.

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A Primer on Design and Data Analysis for Cognitive Pupillometry

Cited by 4 publications

References 87 publications

Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis

Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis

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

Face Detection Using a Capsule Network for Driver Monitoring Application

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