Modeling Cognitive Load in Mobile Human Computer Interaction Using Eye Tracking Metrics

Joseph, Antony William; Vaiz, J. Sharmila; Murugesh, Ramaswami

doi:10.1007/978-3-030-80624-8_13

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Finally, the technologies used (Tobii and Eyelink) in interpreting can introduce varying degrees of cognitive load, potentially influencing accuracy. This result aligns with cognitive psychology and human‐computer interaction findings, elucidating the interplay between technology, cognitive load, and interpretation accuracy (Joseph et al, 2021; Skulmowski & Xu, 2022; Yi et al, 2022).…”

Section: Discussionsupporting

confidence: 84%

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Wang,

Yu,

Wang

2023

WIRES Cognitive Science

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This meta‐analytic research delves into the influence of expertise on cognitive interpreting, emphasizing time efficiency, accuracy, and cognitive effort, in alignment with prevailing expertise theories that link professional development and cognitive efficiency. The study assimilates empirical data from 18 eye‐tracking studies conducted over the past four decades, encompassing a sample of 1581 interpreters. The objective is to elucidate the role of expertise in interpretative performance while tracing the evolution of these dynamics over time. Findings suggest that expert interpreters outperform novices in time efficiency and accuracy and exhibit lower cognitive effort, especially in sight and consecutive interpreting. This effect is particularly pronounced in the English‐Chinese language pair and with the use of E‐prime and Tobii eye‐tracking systems. Further, fixation count and pupil size are essential metrics impacting cognitive effort. These findings have vital implications for interpreter training programs, suggesting a focus on expertise development to enhance efficiency and accuracy, reduce cognitive load, and emphasize the importance of sight interpreting as a foundational skill. The selection of technology and understanding of specific ocular metrics also emerged as essential for future research and practical applications in the interpreting industry.This article is categorized under: Psychology > Theory and Methods Linguistics > Cognitive

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Section: Discussionsupporting

confidence: 84%

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Wang,

Yu,

Wang

2023

WIRES Cognitive Science

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“…These were used for the training of classifiers for the detection of perceived cognitive load. In this study, we chose to employ the traditional machine learning classifiers, namely, decision tree (DT), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), gradient boosting (GB), adaptive gradient boosting (AdaBoost), extreme gradient boosting (XGB), and light gradient boosting (LGB) to estimate cognitive load [53,54]. To simplify the multi-class classification problem, we encoded the perceived mental workload values into low, medium, and high levels [27,28].…”

Section: E Classification Algorithmsmentioning

confidence: 99%

“…Accuracy(%) Cinaz et al [28] 69.8 Zhao et al [27] 72.2 Kakkos et al [56] 82 Marinescu et al [57] 81 Gao et al [55] 74.48 Dolmans et al [8] 77 Tervonen et al [48] 67.6 Joseph et al [53] 81 Our approach 84.24…”

Section: Publicationmentioning

confidence: 99%

Estimating perceived mental workload from eye-tracking data based on benign anisocoria

Chakraborty,

Kiefer,

Raubal

2024

Preprint

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“…Understanding the appearance and dynamics of the human eye has proven to be an essential component of various human-centered research activities and applications, e.g., visual attention modeling [5,80], gaze-based humancomputer interaction [11,19,48], virtual reality [9,55], physical and psychological health monitoring [31,43,51], usability evaluation [29,38], and emotion recognition [4,45]. However, in order to assess human cognitive load or perform other visual attention modeling tasks in real-world situations, it is often required that the evaluation approach should not interfere with the natural behavior of interest such that the mental state of the individual being measured is not influenced by the measurement approach itself.…”

Section: Introductionmentioning

confidence: 99%

CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild

Ding,

Terwilliger,

Parab

et al. 2023

ACM Trans. Comput.-Hum. Interact.

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Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans’ visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis.

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Modeling Cognitive Load in Mobile Human Computer Interaction Using Eye Tracking Metrics

Cited by 5 publications

References 23 publications

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Expertise differences in cognitive interpreting: A meta‐analysis of eye tracking studies across four decades

Estimating perceived mental workload from eye-tracking data based on benign anisocoria

CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild

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