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1. The Index of Pupillary Activity: Measuring Cognitive Load vis-à-vis Task Difficulty with Pupil Oscillation

   https://doi.org/10.1145/3173574.3173856  

   Duchowski, Andrew T.; Krejtz, Krzysztof; Krejtz, Izabela; Biele, Cezary; Niedzielska, Anna; Kiefer, Peter; Raubal, Martin; Giannopoulos, Ioannis (April 2018, Proceedings of the Human Factors in Computing Systems (CHI 2018))

   A novel eye-tracked measure of the frequency of pupil diameter oscillation is proposed for capturing what is thought to be an indicator of cognitive load. The proposed metric, termed the Index of Pupillary Activity, is shown to discriminate task difficulty vis-a-vis cognitive load (if the implied causality can be assumed) in an experiment where participants performed easy and difficult mental arithmetic tasks while fixating a central target (a requirement for replication of prior work). The paper's contribution is twofold: full documentation is provided for the calculation of the proposed measurement which can be considered as an alternative to the existing proprietary Index of Cognitive Activity (ICA). Thus, it is possible for researchers to replicate the experiment and build their own software which implements this measurement. Second, several aspects of the ICA are approached in a more data-sensitive way with the goal of improving the measurement's performance. 

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2. Measuring Cognitive Workload in Augmented Reality Learning Environments Through Pupil Area Analysis

   https://doi.org/10.1007/978-3-031-61569-6_11  

   Mohanty, Siddarth; Kim, Jung Hyup; Pulipati, Varun; Wang, Fang; Mostowfi, Sara; Oprean, Danielle; Wang, Yi; Seo, Kangwon (January 2024, Springer Nature Switzerland)

   In the digital learning landscape, Augmented Reality (AR) is revolutionizing instructional methodologies. This study shifts focus to explore the impact of AR-based lectures on pupil dilation as a biomarker of mental demand. By analyzing pupil dilation with cognitive load assessment tools like the NASA Task Load Index, we aim to understand the cognitive implications of prolonged exposure to AR in educational settings. We hypothesize that variations in pupil size can be indicative of cognitive load, correlating with the mental demands imposed by AR lectures. Preliminary findings suggest a significant relationship between increased pupil dilation and heightened mental workload during AR engagements. This study highlights a new way to measure cognitive workload in AR environments using pupil dilation data. 

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3. Beyond gaze: preliminary analysis of pupil dilation and blink rates in an fMRI study of program comprehension

   https://doi.org/10.1145/3216723.3216726  

   Peitek, Norman; Siegmund, Janet; Parnin, Chris; Apel, Sven; Brechmann, André (January 2018, Proceedings of the Workshop on Eye Movements in Programming (EMIP'18))

   Researchers have been employing psycho-physiological measures to better understand program comprehension, for example simultaneous fMRI and eye tracking to validate top-down comprehension models. In this paper, we argue that there is additional value in eye-tracking data beyond eye gaze: Pupil dilation and blink rates may offer insights into programmers' cognitive load. However, the fMRI environment may influence pupil dilation and blink rates, which would diminish their informative value. We conducted a preliminary analysis of pupil dilation and blink rates of an fMRI experiment with 22 student participants. We conclude from our preliminary analysis that the correction for our fMRI environment is challenging, but possible, such that we can use pupil dilation and blink rates to more reliably observe program comprehension. 

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4. Eye movements as predictors of student experiences during nursing simulation learning events

   https://doi.org/10.1186/s41235-025-00640-7  

   Mason, Madison_Lee; Vatral, Caleb; Cohn, Clayton; Davalos, Eduardo; Jessee, Mary_Ann; Biswas, Gautam; Levin, Daniel_T (July 2025, Cognitive Research: Principles and Implications)

   Abstract Although the “eye-mind link” hypothesis posits that eye movements provide a direct window into cognitive processing, linking eye movements to specific cognitions in real-world settings remains challenging. This challenge may arise because gaze metrics such as fixation duration, pupil size, and saccade amplitude are often aggregated across timelines that include heterogeneous events. To address this, we tested whether aggregating gaze parameters across participant-defined events could support the hypothesis that increased focal processing, indicated by greater gaze duration and pupil diameter, and decreased scene exploration, indicated by smaller saccade amplitude, would predict effective task performance. Using head-mounted eye trackers, nursing students engaged in simulation learning and later segmented their simulation footage into meaningful events, categorizing their behaviors, task outcomes, and cognitive states at the event level. Increased fixation duration and pupil diameter predicted higher student-rated teamwork quality, while increased pupil diameter predicted judgments of effective communication. Additionally, increased saccade amplitude positively predicted students’ perceived self-efficacy. These relationships did not vary across event types, and gaze parameters did not differ significantly between the beginning, middle, and end of events. However, there was a significant increase in fixation duration during the first five seconds of an event compared to the last five seconds of the previous event, suggesting an initial encoding phase at an event boundary. In conclusion, event-level gaze parameters serve as valid indicators of focal processing and scene exploration in natural learning environments, generalizing across event types. 

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5. A Recurrent Neural Network for Attenuating Non-cognitive Components of Pupil Dynamics

   https://doi.org/10.3389/fpsyg.2021.604522  

   Koorathota, Sharath; Thakoor, Kaveri; Hong, Linbi; Mao, Yaoli; Adelman, Patrick; Sajda, Paul (February 2021, Frontiers in Psychology)

   null (Ed.)

   There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal. 

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