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J. Eye Mov. Res., Volume 18, Issue 4 (August 2025) – 6 articles

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18 pages, 1047 KiB

Open AccessArticle

Eye Movement Patterns as Indicators of Text Complexity in Arabic: A Comparative Analysis of Classical and Modern Standard Arabic

by Hend Al-Khalifa

J. Eye Mov. Res. 2025, 18(4), 30; https://doi.org/10.3390/jemr18040030 (registering DOI) - 16 Jul 2025

Abstract

This study investigates eye movement patterns as indicators of text complexity in Arabic, focusing on the comparative analysis of Classical Arabic (CA) and Modern Standard Arabic (MSA) text. Using the AraEyebility corpus, which contains eye-tracking data from readers of both CA and MSA text, we examined differences in fixation patterns, regression rates, and overall reading behavior between these two forms of Arabic. Our analyses revealed significant differences in eye movement metrics between CA and MSA text, with CA text consistently eliciting more fixations, longer fixation durations, and more frequent revisits. Multivariate analysis confirmed that language type has a significant combined effect on eye movement patterns. Additionally, we identified different relationships between text features and eye movements for CA versus MSA text, with sentence-level features emerging as significant predictors across both language types. Notably, we observed an interaction between language type and readability level, with readers showing less sensitivity to readability variations in CA text compared to MSA text. These findings contribute to our understanding of how historical language evolution affects reading behavior and have practical implications for Arabic language education, publishing, and assessment. The study demonstrates the value of eye movement analysis for understanding text complexity in Arabic and highlights the importance of considering language-specific features when studying reading processes. Full article

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19 pages, 1674 KiB

Open AccessArticle

Through the Eyes of the Viewer: The Cognitive Load of LLM-Generated vs. Professional Arabic Subtitles

by Hussein Abu-Rayyash and Isabel Lacruz

J. Eye Mov. Res. 2025, 18(4), 29; https://doi.org/10.3390/jemr18040029 - 14 Jul 2025

Viewed by 58

Abstract

As streaming platforms adopt artificial intelligence (AI)-powered subtitle systems to satisfy global demand for instant localization, the cognitive impact of these automated translations on viewers remains largely unexplored. This study used a web-based eye-tracking protocol to compare the cognitive load that GPT-4o-generated Arabic subtitles impose with that of professional human translations among 82 native Arabic speakers who viewed a 10 min episode (“Syria”) from the BBC comedy drama series State of the Union. Participants were randomly assigned to view the same episode with either professionally produced Arabic subtitles (Amazon Prime’s human translations) or machine-generated GPT-4o Arabic subtitles. In a between-subjects design, with English proficiency entered as a moderator, we collected fixation count, mean fixation duration, gaze distribution, and attention concentration (K-coefficient) as indices of cognitive processing. GPT-4o subtitles raised cognitive load on every metric; viewers produced 48% more fixations in the subtitle area, recorded 56% longer fixation durations, and spent 81.5% more time reading the automated subtitles than the professional subtitles. The subtitle area K-coefficient tripled (0.10 to 0.30), a shift from ambient scanning to focal processing. Viewers with advanced English proficiency showed the largest disruptions, which indicates that higher linguistic competence increases sensitivity to subtle translation shortcomings. These results challenge claims that large language models (LLMs) lighten viewer burden; despite fluent surface quality, GPT-4o subtitles demand far more cognitive resources than expert human subtitles and therefore reinforce the need for human oversight in audiovisual translation (AVT) and media accessibility. Full article

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21 pages, 2624 KiB

Open AccessArticle

GMM-HMM-Based Eye Movement Classification for Efficient and Intuitive Dynamic Human–Computer Interaction Systems

by Jiacheng Xie, Rongfeng Chen, Ziming Liu, Jiahao Zhou, Juan Hou and Zengxiang Zhou

J. Eye Mov. Res. 2025, 18(4), 28; https://doi.org/10.3390/jemr18040028 - 9 Jul 2025

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Abstract

Human–computer interaction (HCI) plays a crucial role across various fields, with eye-tracking technology emerging as a key enabler for intuitive and dynamic control in assistive systems like Assistive Robotic Arms (ARAs). By precisely tracking eye movements, this technology allows for more natural user interaction. However, current systems primarily rely on the single gaze-dependent interaction method, which leads to the “Midas Touch” problem. This highlights the need for real-time eye movement classification in dynamic interactions to ensure accurate and efficient control. This paper proposes a novel Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) classification algorithm aimed at overcoming the limitations of traditional methods in dynamic human–robot interactions. By incorporating sum of squared error (SSE)-based feature extraction and hierarchical training, the proposed algorithm achieves a classification accuracy of 94.39%, significantly outperforming existing approaches. Furthermore, it is integrated with a robotic arm system, enabling gaze trajectory-based dynamic path planning, which reduces the average path planning time to 2.97 milliseconds. The experimental results demonstrate the effectiveness of this approach, offering an efficient and intuitive solution for human–robot interaction in dynamic environments. This work provides a robust framework for future assistive robotic systems, improving interaction intuitiveness and efficiency in complex real-world scenarios. Full article

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35 pages, 2865 KiB

Open AccessArticle

eyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification

by Michael Barz, Omair Shahzad Bhatti, Hasan Md Tusfiqur Alam, Duy Minh Ho Nguyen, Kristin Altmeyer, Sarah Malone and Daniel Sonntag

J. Eye Mov. Res. 2025, 18(4), 27; https://doi.org/10.3390/jemr18040027 - 7 Jul 2025

Viewed by 273

Abstract

Mobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We measure the perceived usability, annotations’ validity and reliability, and efficiency during a data annotation task. We asked our participants to re-annotate data from a single individual using an existing dataset (n = 48). Further, we conducted a semi-structured interview to understand how participants used the provided IML features and assessed our design decisions. In a post hoc experiment, we investigate the performance of three image classification models in annotating data of the remaining 47 individuals. Full article

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14 pages, 5319 KiB

Open AccessArticle

Efficiency Analysis of Disruptive Color in Military Camouflage Patterns Based on Eye Movement Data

by Xin Yang, Su Yan, Bentian Hao, Weidong Xu and Haibao Yu

J. Eye Mov. Res. 2025, 18(4), 26; https://doi.org/10.3390/jemr18040026 - 2 Jul 2025

Viewed by 220

Abstract

Disruptive color on animals’ bodies can reduce the risk of being caught. This study explores the camouflaging effect of disruptive color when applied to military targets. Disruptive and non-disruptive color patterns were placed on the target surface to form simulation materials. Then, the simulation target was set in woodland-, grassland-, and desert-type background images. The detectability of the target in the background was obtained by collecting eye movement indicators after the observer observed the background targets. The influence of background type (local and global), camouflage pattern type, and target viewing angle on the disruptive-color camouflage pattern was investigated. This study aims to design eye movement observation experiments to statistically analyze the indicators of first discovery time, discovery frequency, and first-scan amplitude in the target area. The experimental results show that the first discovery time of mixed disruptive-color targets in a forest background was significantly higher than that of non-mixed disruptive-color targets (t = 2.54, p = 0.039), and the click frequency was reduced by 15% (p < 0.05), indicating that mixed disruptive color has better camouflage effectiveness in complex backgrounds. In addition, the camouflage effect of mixed disruptive colors on large-scale targets (viewing angle ≥ 30°) is significantly improved (F = 10.113, p = 0.01), providing theoretical support for close-range reconnaissance camouflage design. Full article

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20 pages, 1015 KiB

Open AccessArticle

Improving Reading and Eye Movement Control in Readers with Oculomotor and Visuo-Attentional Deficits

by Stéphanie Ducrot, Bernard Lété, Marie Vernet, Delphine Massendari and Jérémy Danna

J. Eye Mov. Res. 2025, 18(4), 25; https://doi.org/10.3390/jemr18040025 - 23 Jun 2025

Viewed by 322

Abstract

The initial saccade of experienced readers tends to land halfway between the beginning and the middle of words, at a position originally referred to as the preferred viewing location (PVL). This study investigated whether a simple physical manipulation—namely, increasing the saliency (brightness or color) of the letter located at the PVL—can positively influence saccadic targeting strategies and optimize reading performance. An eye-movement experiment was conducted with 25 adults and 24 s graders performing a lexical decision task. Results showed that this manipulation had no effect on initial landing positions in proficient readers, who already landed most frequently at the PVL, suggesting that PVL saliency is irrelevant once automatized saccade targeting routines are established. In contrast, the manipulation shifted the peak of the landing site distribution toward the PVL for a cluster of readers with immature saccadic strategies (with low reading-level scores and ILPs close to the beginning of words), but only in the brightness condition, and had a more compelling effect in a cluster with oculomotor instability (with flattened and diffuse landing position curves along with oculomotor and visuo-attentional deficits). These findings suggest that guiding the eyes toward the PVL may offer a novel way to improve reading efficiency, particularly for individuals with oculomotor and visuo-attentional difficulties. Full article

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