Journal of Eye Movement Research最新文献

Strong communication is central to the translation of breast cancer screening availability into uptake. This experiment tests the role of design features of screening advertisements in directing visual attention in screening-eligible women (≥40 years). To this end, a within-subjects eye-tracking experiment (N = 30) was conducted in which women viewed six static public service advertisements. Predefined Areas of Interest (AOIs), Text, Image/Visual, Symbol, Logo, Website/CTA, and Source/Authority-were annotated, and three standard measures were calculated: Time to First Fixation (TTFF), Fixation Count (FC), and Fixation Duration (FD). Analyses combined descriptive summaries with subgroup analyses using nonparametric methods and generalized linear mixed models (GLMMs) employing participant-level random intercepts. Within each category of stimuli, detected differences were small in magnitude yet trended towards few revisits in each category for the FC mode; TTFF and FD showed no significant differences across categories. Viewing data from the perspective of Areas of Interest (AOIs) highlighted pronounced individual differences. Narratives/efficacy text and dense icon/text callouts prolonged processing times, although institutional logos and abstract/anatomical symbols generally received brief treatment except when coupled with action-oriented communication triggers. TTFF timing also tended toward individual areas of interest aligned with the Scan-Then-Read strategy, in which smaller labels/sources/CTAs are exploited first in comparison with larger headlines/statistical text. Practically, screening messages should co-locate access and credibility information in early-attention areas and employ brief, fluent efficacy text to hold gaze. The study adds PSA-specific eye-tracking evidence for breast cancer screening and provides immediately testable design recommendations for programs in Greece and the EU.

Mobile games have become one of the fastest-growing segments of the digital economy, and in-app advertisements represent a major source of revenue while shaping consumer attention and memory processes. This study examined the relationship between visual attention and brand recall of in-app advertisements in a mobile sports game using mobile eye-tracking technology. A total of 79 participants (47 male, 32 female; Mage = 25.8) actively played a mobile sports game for ten minutes while their eye movements were recorded with Tobii Pro Glasses 2. Areas of interest (AOIs) were defined for embedded advertisements, and fixation-related measures were analyzed. Brand recall was assessed through unaided, verbal-aided, and visual-aided measures, followed by demographic comparisons based on gender, mobile sports game experience and interest in tennis. Results from Generalized Linear Mixed Models (GLMMs) revealed that brand placement was the strongest predictor of recall (p < 0.001), overriding raw fixation duration. Specifically, brands integrated into task-relevant zones (e.g., the central net area) achieved significantly higher recall odds compared to peripheral ads, regardless of marginal variations in dwell time. While eye movement metrics varied by gender and interest, the multivariate model confirmed that in active gameplay, task-integration drives memory encoding more effectively than passive visual salience. These findings suggest that active gameplay imposes unique cognitive demands, altering how attention and memory interact. The study contributes both theoretically by extending advertising research into ecologically valid gaming contexts and practically by informing strategies for optimizing mobile in-app advertising.

Major depressive disorder (MDD) represents a prevalent mental health condition characterized by prominent attentional biases, particularly toward negative stimuli. While extensive research has established the significance of negative attentional bias in depression, critical gaps remain in understanding the temporal dynamics and valence-specificity of these biases. This study employed eye-tracking technology to systematically examine the attentional processing of emotional faces (happy, fearful, sad) in MDD patients (n = 61) versus healthy controls (HC, n = 47), assessing both the initial orientation (initial gaze preference) and sustained attention (first dwell time). Key findings revealed the following: (1) while both groups showed an initial vigilance toward threatening faces (fearful/sad), only MDD patients displayed an additional attentional capture by happy faces; (2) a significant emotion main effect (F (2, 216) = 10.19, p < 0.001) indicated a stronger initial orientation to fearful versus happy faces, with Bayesian analyses (BF < 0.3) confirming the absence of group differences; and (3) no group disparities emerged in sustained attentional maintenance (all ps > 0.05). These results challenge conventional negativity-focused models by demonstrating valence-specific early-stage abnormalities in MDD, suggesting that depressive attentional dysfunction may be most pronounced during initial automatic processing rather than later strategic stages. The findings advance the theoretical understanding of attentional bias in depression while highlighting the need for stage-specific intervention approaches.

Eye-tracking for user experience analysis has traditionally relied on dedicated hardware, which is often costly and imposes restrictive operating conditions. As an alternative, solutions utilizing ordinary webcams have attracted significant interest due to their affordability and ease of use. However, a major limitation persists in these vision-based methods: sensitivity to head movements. Therefore, users are often required to maintain a rigid head position, leading to discomfort and potentially skewed results. To address this challenge, this paper proposes a robust eye-tracking methodology designed to accommodate head motion. Our core technique involves mapping the displacement of the pupil center from a dynamically updated reference point to estimate the gaze point. When head movement is detected, the system recalculates the head-pointing coordinate using estimated head pose and user-to-screen distance. This new head position and the corresponding pupil center are then established as the fresh benchmark for subsequent gaze point estimation, creating a continuous and adaptive correction loop. We conducted accuracy tests with 22 participants. The results demonstrate that our method surpasses the performance of many current methods, achieving mean gaze errors of 1.13 and 1.37 degrees in two testing modes. Further validation in a smooth pursuit task confirmed its efficacy in dynamic scenarios. Finally, we applied the method in a real-world gaming context, successfully extracting fixation counts and gaze heatmaps to analyze visual behavior and UX across different game modes, thereby verifying its practical utility.

Distance simultaneous interpreting is a typical example of technology-mediated interpreting, bridging participants (i.e., interpreters, audience, and speakers) in various events and conferences. This study explores how presentation mode affects cognitive load in DSI, utilizing eye-tracking sensor technology. A controlled experiment was conducted involving 36 participants, comprising 19 professional interpreters and 17 student interpreters, to assess the effects of presentation mode on their cognitive load during English-to-Chinese DSI. A Tobii Pro X3-120 screen-based eye tracker was used to collect eye-tracking data as the participants sequentially performed a DSI task involving four distinct presentation modes: the Speaker, Slides, Split, and Corner modes. The findings, derived from the integration of eye-tracking data and interpreting performance scores, indicate that both presentation mode and experience level significantly influence interpreters' cognitive load. Notably, student interpreters demonstrated longer fixation durations in the Slides mode, indicating a reliance on visual aids for DSI. These results have implications for language learning, suggesting that the integration of visual supports can aid in the acquisition and performance of interpreting skills, particularly for less experienced interpreters. This study contributes to our understanding of the interplay between technology, cognitive load, and language learning in the context of DSI.

Mental effort, a critical factor influencing task performance, is often difficult to measure accurately and efficiently. Pupil diameter has emerged as a reliable, real-time indicator of mental effort. This study introduces RIPA2, an enhanced pupillometric index for real-time mental effort assessment. Building on the original RIPA method, RIPA2 incorporates refined Savitzky-Golay filter parameters to better isolate pupil diameter fluctuations within biologically relevant frequency bands linked to cognitive load. We validated RIPA2 across two distinct tasks: a structured N-back memory task and a naturalistic information search task involving fact-checking and decision-making scenarios. Our findings show that RIPA2 reliably tracks variations in mental effort, demonstrating improved sensitivity and consistency over the original RIPA and strong alignment with the established offline measures of pupil-based cognitive load indices, such as LHIPA. Notably, RIPA2 captured increased mental effort at higher N-back levels and successfully distinguished greater effort during decision-making tasks compared to fact-checking tasks, highlighting its applicability to real-world cognitive demands. These findings suggest that RIPA2 provides a robust, continuous, and low-latency method for assessing mental effort. It holds strong potential for broader use in educational settings, medical environments, workplaces, and adaptive user interfaces, facilitating objective monitoring of mental effort beyond laboratory conditions.

Social media has become a dominant channel for food marketing, particularly targeting youth through visually engaging and socially embedded content. This study investigates how young adults visually engage with food advertisements on social media and how specific visual and contextual features influence purchase intention. Using eye-tracking technology and survey analysis, data were collected from 35 participants aged 18 to 25. Participants viewed simulated Instagram posts incorporating elements such as food imagery, branding, influencer presence, and social cues. Visual attention was recorded using Tobii Pro Spectrum, and behavioral responses were assessed via post-surveys. A 2 × 2 design varying influencer presence and food type showed that both features significantly increased visual attention. Marketing cues and branding also attracted substantial visual attention. Linear regression revealed that core/non-core content and influencer features were among the strongest predictors of consumer response. The findings underscore the persuasive power of human and social features in digital food advertising. These insights have implications for commercial marketing practices and for understanding how visual and social elements influence youth engagement with food content on digital platforms.

Purpose: An observational study to investigate differences in gaze behaviors across varying expertise levels using a 3D heads-up display (HUD) integrated with eye-tracking was conducted. Methods: 25 ophthalmologists (PGY2-4, fellows, attendings; number(n) = 5/group) performed cataract surgery on a SimulEYE model using NGENUITY HUD. Results: Surgical proficiency increased with experience, with attendings achieving the highest scores (54.4 ± 0.89). Compared with attendings, PGY2s had longer fixation durations (p = 0.042), longer saccades (p < 0.0001), and fewer fixations on the HUD (p < 0.0001). Capsulorhexis diameter relative to capsule size increased with expertise, with fellows and attendings achieving significantly larger diameters than PGY2s (p < 0.0001). Experts maintained smaller tear angles, initiated tears closer to the main wound, and produced more circular morphologies. They rapidly alternated gaze between instruments and surrounding tissue, whereas novices (PGY2-4) fixated primarily on the instrument tip. Conclusions: Experts employ a feed-forward visual sampling strategy, allowing perception of instruments and surrounding tissue, minimizing inadvertent damage. Furthermore, attending surgeons maintain smaller tear angles and initiate tears proximally to forceps insertion, which may contribute to more controlled tears. Future integration of eye-tracking technology into surgical training could enhance visual-motor strategies in novices.

Recent breakthroughs in machine learning, artificial intelligence, and the emergence of large datasets have made the integration of eye tracking increasingly feasible not only in computing but also in many other disciplines to accelerate innovation and scientific discovery. These transformative changes often depend on intelligently analyzing and interpreting gaze data, which demand a substantial technical background. Overcoming these technical barriers has remained an obstacle to the broader adoption of eye tracking technologies in certain communities. In an effort to increase accessibility that potentially empowers a broader community of researchers and practitioners to leverage eye tracking, this paper presents an open-source software platform: Beach Environment for the Analytics of Human Gaze (BEACH-Gaze), designed to offer comprehensive descriptive and predictive analytical support. Firstly, BEACH-Gaze provides sequential gaze analytics through window segmentation in its data processing and analysis pipeline, which can be used to achieve simulations of real-time gaze-based systems. Secondly, it integrates a range of established machine learning models, allowing researchers from diverse disciplines to generate gaze-enabled predictions without advanced technical expertise. The overall goal is to simplify technical details and to aid the broader community interested in eye tracking research and applications in data interpretation, and to leverage knowledge gained from eye gaze in the development of machine intelligence. As such, we further demonstrate three use cases that apply descriptive and predictive gaze analytics to support individuals with autism spectrum disorder during technology-assisted exercises, to dynamically tailor visual cues for an individual user via physiologically adaptive visualizations, and to predict pilots' performance in flight maneuvers to enhance aviation safety.

Purpose: This study evaluates a novel, non-invasive method using a virtual reality (VR) headset with integrated eye trackers to assess retinal function by measuring the recovery of the pupillary response after light adaptation in patients with age-related macular degeneration (AMD). Methods: In this pilot study, fourteen patients with clinically confirmed AMD and 14 age-matched healthy controls were exposed to alternating bright and dark stimuli using a VR headset. The dark stimulus duration increased incrementally by 100 milliseconds per trial, repeated over 50 cycles. The pupillary response to the re-onset of brightness was recorded. Data were analyzed using a linear mixed-effects model to compare recovery patterns between groups and a convolutional neural network to evaluate diagnostic accuracy. Results: The pupillary response amplitude increased with longer dark stimuli, i.e., the longer the eye was exposed to darkness the bigger was the subsequent pupillary amplitude. This pupillary recovery was significantly slowed by age and by the presence of macular degeneration. Test diagnostic accuracy for AMD was approximately 92%, with a sensitivity of 90% and a specificity of 70%. Conclusions: This proof-of-concept study demonstrates that consumer-grade VR headsets with integrated eye tracking can detect retinal dysfunction associated with AMD. The method offers a fast, accessible, and potentially scalable approach for retinal disease screening and monitoring. Further optimization and validation in larger cohorts are needed to confirm its clinical utility.