Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Skaramagkas, Vasileios; Giannakakis, Giorgos; Ktistakis, Emmanouil; Manousos, Dimitris; Tachos, Nikolaos S.; Tripoliti, Evanthia E.; Marias, Kostas; Fotiadis, Dimitrios I.; Tsiknakis, Manolis

doi:10.1109/rbme.2021.3066072

Cited by 93 publications

(59 citation statements)

References 199 publications

(187 reference statements)

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“…At least certain aspects of ocular behavior are relatively free of conscious control (e.g., early trajectory of eye movements; Theeuwes et al, 1998) and we can also speculate that the cognitively demanding nature of deception (Giolla & Luke, 2021; Vrij et al, 2017; Walczyk et al, 2018, pp. 6–8) may impact eye-movements (for links between increased cognitive load and eye behavior, see Eckstein et al, 2017; Skaramagkas et al, 2021; van Gog et al, 2009, pp. 327–328).…”

Section: Discussionmentioning

confidence: 99%

Feigned ADHD Associated Cognitive Impairment: Utility of Integrating an Eye-tracker and the MOXO-dCPT

et al. 2021

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Objective: The current study assessed the utility of eye-movements measures, gathered while participants performed a commercially available Continuous Performance Test (CPT), to detect feigned ADHD-associated cognitive impairment. Method: Healthy simulators ( n = 37), ADHD patients ( n = 33), and healthy controls ( n = 36) performed an eye-tracker integrated MOXO-dCPT and a stand-alone validity indicator. Results: Simulators gazed significantly longer at regions that were irrelevant for successful MOXO-dCPT performance compared to ADHD patients and healthy controls. This eye-movement measure, however, had lower sensitivity than traditional MOXO-dCPT indices. Discussion: Gaze direction measures, gathered while performing a CPT, show initial promise as validity indicators. Traditional CPT measures, however, are more sensitive and therefore offer a more promising path for the establishment of CPT-based validity indicators. The current study is an initial exploration of the issue and further evaluation of both theoretical and practical aspects is mandated.

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Section: Discussionmentioning

confidence: 99%

Feigned ADHD Associated Cognitive Impairment: Utility of Integrating an Eye-tracker and the MOXO-dCPT

et al. 2021

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“…Pupil size seems to be the most useful indicator of cognitive load. However, blink and saccade related features also seem to be correlated with the cognitive workload [6].…”

Section: Introductionmentioning

confidence: 98%

Cognitive workload level estimation based on eye tracking: A machine learning approach

Skaramagkas

Ktistakis

Manousos

et al. 2021

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)

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Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.

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“…ECENTLY, advanced eye tracking technology has enabled convenient and timely quantification of the human gaze data [1]. The eye-tracker and measured eye movements have been widely applied in various research fields, including vision [2], emotional and cognitive processes [3], neurology (e.g., dementia) [4], and psychiatry (e.g., depression, autism spectrum disorder (ASD), etc.) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Reader’s English Level From Reading Fixation Patterns Using the Siamese Convolutional Neural Network

Fan

Cao

Meng

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Among numerous functions performed by the human eye, reading is a common task that best reflects an individual's understanding and cognitive patterns. Previous studies showed that text comprehension may be determined by comprehension monitoring, a metacognitive process that evaluates and regulates the pattern of comprehension. Herein, we propose a hypothesis: an individual's cognitive pattern during reading is predictive of the level of reading comprehension. According to the criteria of the College English Test Band Six (CET-6), 80 participants (sophomore and junior) were divided into a pass group (n = 40) and a non-pass group (n = 40). Heatmaps of eye fixation counts were collected by an eye-tracker while each participant executed four reading comprehension tests. Using these heatmaps as inputs, we proposed the Siamese convolutional neural network models to predict the English level of participants. Both strategies of "Trained from scratch" and "Pretrained with fine-tuning" were employed. "Soft Voting" was applied to integrate the predictions from four tests. Results showed that the Siamese network model trained by the datasets with the cluster radius of fixation equal to 25 pixels and connection layer by L1 norm distance has a satisfactory or superior performance to other comparative experiments. The AUC values of Siamese networks trained by the two strategies reach 0.941 and 0.956, respectively. This indicates that the individual reading cognitive pattern captured by the eye-tracker can predict the level of reading comprehension through advanced deep learning models.

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Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Cited by 93 publications

References 199 publications

Feigned ADHD Associated Cognitive Impairment: Utility of Integrating an Eye-tracker and the MOXO-dCPT

Feigned ADHD Associated Cognitive Impairment: Utility of Integrating an Eye-tracker and the MOXO-dCPT

Cognitive workload level estimation based on eye tracking: A machine learning approach

Predicting the Reader’s English Level From Reading Fixation Patterns Using the Siamese Convolutional Neural Network

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