Eye Tracking Research Articles

Attentional mechanisms of the date/delay effect in intertemporal choice: An eye-tracking study.

Temporal discounting refers to the tendency to discount future rewards as a function of time until receipt of rewards. The discount rate can be reduced by experimentally manipulating time framing, an example being the date/delay effect: Specifically, if time until receipt of the reward is presented as a date (e.g., August 21, 2022) rather than as a delay (e.g., 136 days), temporal discounting is reduced. While this effect has been replicated several times, its underlying cognitive mechanisms are not well understood. Therefore, we used eye tracking to examine the role of attention in the date/delay effect. Participants completed both a delay and date condition of the Monetary Choice Questionnaire, while eye movements were recorded (N = 54). Results revealed a successful replication of the date/delay effect (p < .001, gav = 0.48). Eye tracking showed that participants compared time attributes (relative to reward attributes) more and fixated them longer in the date compared to the delay condition. Moreover, the absolute difference in reward values of choice options was more predictive of choosing the delayed reward in the date compared to the delay condition. Finally, explorative correlations revealed a stronger date/delay effect in participants who paid more attention to time than reward attributes in the delay condition and who used a more integrative search strategy. Our findings suggest that the date manipulation causes participants to weight rewards more strongly in their decision process than in the delay condition, ultimately reducing temporal discounting. Computation of time intervals in the date condition could possibly reflect an adaptation lowering the date/delay effect. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effects of driver’s braking behavior by the real-time pedestrian scale warning system

A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian’s gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments.

Head motion-corrected eye gaze tracking with the da Vinci surgical system.

To facilitate the integration of point of gaze (POG) as an input modality for robot-assisted surgery, we introduce a robust head movement compensation gaze tracking system for the da Vinci Surgical System. Previous surgical eye gaze trackers require multiple recalibrations and suffer from accuracy loss when users move from the calibrated position. We investigate whether eye corner detection can reduce gaze estimation error in a robotic surgery context. A polynomial regressor is first used to estimate POG after an 8-point calibration, and then, using another regressor, the POG error from head movement is estimated from the shift in 2D eye corner location. Eye corners are computed by first detecting regions of interest using the You Only Look Once (YOLO) object detector trained on 1600 annotated eye images (open dataset included). Contours are then extracted from the bounding boxes and a derivative-based curvature detector refines the eye corner. Through a user study (n = 24), our corner-contingent head compensation algorithm showed an error reduction in degrees of visual angle of 1.20 (p = 0.037) for the left eye and 1.26 (p = 0.079) for the right compared to the previous gold-standard POG error correction method. In addition, the eye corner pipeline showed a root-mean-squared error of 3.57 (SD = 1.92) pixels in detecting eye corners over 201 annotated frames. We introduce an effective method of using eye corners to correct for eye gaze estimation, enabling the practical acquisition of POG in robotic surgery.

Practical aspects of measuring camera‐based indicators of alcohol intoxication in manual and automated driving

AbstractCamera‐based Driver Monitoring Systems (DMS) have the potential to exploit eye tracking correlates of alcohol intoxication to detect drunk driving. This study investigates how glance, blink, saccade, and fixation metrics are affected by alcohol, and whether possible effects remain stable across three different camera setups, as well as when the driver is out‐of‐the‐loop during level 4 automated driving (Wizard‐of‐Oz setup). Thirty‐five participants drove on a test track first sober and then with increasing intoxication levels reaching a breath alcohol concentration (BrAC) of 1‰. Linear Mixed‐Effects Regression analyses showed that with increasing intoxication levels, eye blinks became longer and slower, glances and fixations became fewer and longer, and more attention was directed to the road area, at the expense of more peripheral areas. Fixation and blink metrics were more robust to changes in automation mode, whereas glance‐based metrics were highly context dependent. Not all effects of alcohol intoxication could be measured with all eye tracking setups, where one‐camera systems showed lower data availability and higher noise levels compared to a five‐camera system. This means that lab findings based on higher quality eye tracking data might not be directly applied to production settings because of hardware limitations.

Eye-Tracking-basierte Analyse des menschlichen Verhaltens

Abstract Over the last 10 years, production systems have been shaped by Industry 4.0, which drives digitalisation and networking in real time. Industry 5.0, on the other hand, focuses on human-centred technologies that support employees and enhance their skills. Personalisation plays a major role here, especially in manual assembly and testing workstations. The challenge is to adapt workstations individually without compromising standardisation. Eye tracking is used to investigate influencing factors at the workplace and analyse the data using software. This research forms the basis for a concept for the personalisation of manual workstations.

Investigating the Readability of School Geographic Map Symbols Using Eye-Tracking Technology

Geography atlas is an educational material that is very important in geography education. For the eye tracking experiment, six mineral maps were selected from different geography atlases for high school students. The aim of this study was to analyze which design of conventional symbols is more understandable for high school students. We hope that the presented methodology and the case study will help mapmakers to create more effective school geographic maps.

Prospective Distractor Information Reduces Reward-Related Attentional Capture.

Motivationally salient stimuli, such as those associated with reward, can automatically gain attentional prioritisation - even when individuals are motivated to ignore such stimuli. This 'attentional bias for reward' has often been interpreted as evidence for involuntary Pavlovian 'sign tracking' behaviour. The prioritisation of reward-signalling distractors may additionally reflect a drive to gain information about the state of the world, irrespective of the particular reward that is being signalled. In the current study we assessed whether forewarning participants on each trial as to the upcoming features of a distractor would reduce reward-related attentional capture. This manipulation reduces the information provided by the distractor, without affecting the magnitude of the signalled reward. Using eye tracking in Experiment 1, we found that reward-related attentional capture was virtually eliminated when participants were informed of the upcoming distractor colour (relative to the baseline condition when no information was provided). In Experiment 2, using a response-time version of the task, we again found a significant reduction in reward-related attentional capture when participants received information about the colour of an upcoming distractor, or information about the value of the upcoming reward. Finally, in Experiment 3 we assessed whether participants were using the pre-trial information to strategically inhibit attention to the upcoming distractor colour. The results of these experiments are discussed within the context of information-seeking accounts of reward-related attentional capture effects.

Deep face profiler (DeFaP): Towards explicit, non-restrained, non-invasive, facial and gaze comprehension

Eye tracking and head pose estimation (HPE) have previously lacked reliability, interpretability, and comprehensibility. For instance, many works rely on traditional computer vision methods, which may not perform well in dynamic and realistic environments. Recently, a widespread trend has emerged, leveraging deep learning for HPE specifically framed as a regression task; however, considering the real-time applications, the problem could be better formulated as classification (e.g., left, centre, right head pose and gaze) using a hybrid approach. For the first time, we present a complete facial profiling approach to extract micro and macro facial movement, gaze, and eye state features, which can be used for various applications related to comprehension analysis. The multi-model approach provides discrete human-understandable head pose estimations utilising deep transfer learning, a newly introduced method of head roll calculation, gaze estimation via iris detection, and eye state estimation (i.e., open or closed). Unlike existing works, this approach can automatically analyse the input image or video frame to produce human-understandable binary codes (e.g., eye open or close, looking left or right, etc.) for each facial component (aka face channels). The proposed approach is validated on multiple standard datasets, indicating outperformance compared to existing methods in several aspects, including reliability, generalisation, completeness, and interpretability. This work will significantly impact several diverse domains, including psychological and cognitive tasks with a broad scope of applications, such as in police interrogations and investigations, animal behaviour, and smart applications, including driver behaviour analysis, student attention measurement, and automated camera flashes.

Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education.

The aim of this study was to perform a comprehensive bibliometric analysis of virtual reality (VR) and augmented reality (AR) applications in dental education. A cross-sectional research was carried out using a bibliometric methodology. This process entailed the assessment of metadata from scientific publications that are catalogued in the Scopus database, covering the period from January 2018 to August 2023. A variety of indicators were utilized to scrutinize scientific production and dissemination within the academic community. These encompassed elements such as the author, the publication itself, the number of citations, institutional and collaborative affiliations, geographical location, journal quartile ranking, h-index, Source Normalized Impact per Paper (SNIP), Field-Weighted Citation Impact (FWCI), SCImago Journal Rank (SJR), and the CiteScore. Several institutions from different countries and their academic output were found. Beihang University stands out with 16 scholarly articles, followed by Stanford University with 16 articles and 170 citations. The Q1 quartile has experienced a steady increase, reaching 87 scientific articles. The top 10 authors in scientific production on augmented and VR in dentistry include Joe Amal Cecil, Avinash Gupta, and Miguel A Pirela-Cruz. In terms of co-authorship by country, the United States, Germany, and China are the most predominant in the clusters represented. However, other clusters also have a significant presence. By analyzing the explored trends and themes of keyword co-occurrence, four main clusters were identified. The yellow cluster contained the largest amount of research with the keyword "virtual reality." In addition, the blue cluster was found to be best related to the green "simulation," purple "virtual reality (VR)," and light blue "human-centered computing" clusters. This study evidenced the availability and quality of the data used for the analysis. Future studies could consider the use of VR systems with integrated eye tracking and compare their effect in dentistry during dental procedures. The clinical importance of this study lies in its potential to improve dental education. The VR and AR can provide dental students with immersive, hands-on learning experiences, which can enhance their understanding and clinical skills. Furthermore, the translational value of this study extends beyond dental education. The insights gained from this research could be applicable to other fields of medical education where hands-on training is crucial. Thus, the findings of this study have the potential to influence the broader landscape of medical education, ultimately leading to improved healthcare outcomes. How to cite this article: Alvitez-Temoche D, Silva H, Aguila ED, et al. Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education. J Contemp Dent Pract 2024;25(4):358-364.

Eye tracking technology on children's mathematical education: systematic review

Eye tracking technology on children's mathematical education: systematic review

Quality Analysis of SMA Negeri 1 Sokaraja Website using Eye Tracking Method

The role of websites in academic management and enhancing the reputation of a company or institution is very important. The quality of website services also has a crucial role in the world of education. Based on the results of interviews with the person in charge of the SMA Negeri 1 Sokaraja website, it was found that there were problems, namely the satisfaction of website users was not yet known and there were several pages that were not responsive, such as in the homepage/home area, which needed to be improved or optimized. This research involves a number of respondents who will give their views on the SMA Negeri 1 Sokaraja website. The analysis results obtained from this research are expected to provide a clear understanding of the extent to which this website meets the quality standards set by the Eye Tracking method. In addition, the analysis results will also provide a detailed evaluation of each aspect of the website's quality. In this research, the Eye Tracking method is used to measure where users look, how long they look, and the order in which they look. There are aspects of usability measurement, namely efficiency, effectiveness and satisfaction. SMA Negeri 1 Sokaraja website users are the respondents of this research. The results of usability testing with the USE Questionier method with a feasibility percentage of 84.17% with a very feasible category which means the website is very feasible for users. The results of usability testing with the eye-tracking method that each task is well received by users, but the effectiveness of delivering information reaches a score of 73.14, this score is classified as quite effective

A review of machine learning in scanpath analysis for passive gaze-based interaction.

The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.

Attentional suppression of dynamic versus static salient distractors.

Attention must be carefully controlled to avoid distraction by salient stimuli. The signal suppression hypothesis proposes that salient stimuli can be proactively suppressed to prevent distraction. Although this hypothesis has garnered much support, most previous studies have used one class of salient distractors: color singletons. It therefore remains unclear whether other kinds of salient distractors can also be suppressed. The current study directly compared suppression of a variety of salient stimuli using an attentional capture task that was adapted for eye tracking. The working hypothesis was that static salient stimuli (e.g., color singletons) would be easier to suppress than dynamic salient stimuli (e.g., motion singletons). The results showed that participants could ignore a wide variety of salient distractors. Importantly, suppression was weaker and slower to develop for dynamic salient stimuli than static salient stimuli. A final experiment revealed that adding a static salient feature to a dynamic motion distractor greatly improved suppression. Altogether, the results suggest that an underlying inhibitory process is applied to all kinds of salient distractors, but that suppression is more readily applied to static features than dynamic features.

Peripheral vision contributes to implicit attentional learning: Findings from the "mouse-eye" paradigm.

The central visual field is essential for activities like reading and face recognition. However, the impact of peripheral vision loss on daily activities is profound. While the importance of central vision is well established, the contribution of peripheral vision to spatial attention is less clear. In this study, we introduced a "mouse-eye" method as an alternative to traditional gaze-contingent eye tracking. We found that even in tasks requiring central vision, peripheral vision contributes to implicit attentional learning. Participants searched for a T among Ls, with the T appearing more often in one visual quadrant. Earlier studies showed that participants' awareness of the T location probability was not essential for their ability to learn. When we limited the visible area around the mouse cursor, only participants aware of the target's location probability showed learning; those unaware did not. Adding placeholders in the periphery did not restore implicit attentional learning. A control experiment showed that when participants were allowed to see all items while searching and moving the mouse to reveal the target's color, both aware and unaware participants acquired location probability learning. Our results underscore the importance of peripheral vision in implicitly guided attention. Without peripheral vision, only explicit, but not implicit, attentional learning prevails.

Reading with Screen Magnification: Eye Movement Analysis Using Compensated Gaze Tracks.

Eye movements while reading with screen magnification (which requires manual scrolling to center the magnified portion of the screen within the viewport) pose interpretation challenges. Standard representations in terms of alternating fixations and saccades don't apply to this case. This is because, during scrolling, eyes often track a moving text element, generating a movement akin to smooth pursuit. We propose a new representation that uses information from the mouse (which the reader uses to move the center of magnification) to undo the effect of magnification and scrolling. After this "compensation" operation, gaze tracks can again be described as alternating fixations and saccades. We present an analysis of gaze tracks obtained by applying this transformation on an existing dataset, recorded from low vision readers using two modalities of screen magnification. This analysis highlights similarities and differences in terms of dynamic properties of compensated gaze tracks vis-à-vis gaze during regular reading.

Student visual attention during group instruction phases in collaborative geometry problem solving

AbstractTeacher gestures support mathematics learning and promote student collaboration. Aligned with speech, gestures can help students to notice the important visual information of geometry tasks. However, students’ visual attention to the teacher’s gestural cues during collaborative problem solving remains a largely unexplored field in mathematics education research. This mixed-method case study investigated relations between students’ visual attention, teacher gestures, and students’ collaborative problem-solving process on a geometry task. The data were collected with video cameras and mobile gaze trackers on four students simultaneously in two Finnish 9th-grade mathematics lessons with the same teacher. The findings show that the students attended to their own papers most of the time during the teacher's gestures, but differences in student attention between the gesture types emerged. The qualitative analysis showed that the teacher’s tracing, pointing, and representational gestures helped in directing student attention to targets relevant to the situational learning process. We conclude that teacher gestures can both convey mathematical contents and direct student attention, which intermediates mathematical thinking in problem solving.

A Functional Usability Analysis of Appearance-Based Gaze Tracking for Accessibility.

Appearance-based gaze tracking algorithms, which compute gaze direction from user face images, are an attractive alternative to infrared-based external devices. Their accuracy has greatly benefited by using powerful machine-learning techniques. The performance of appearance-based algorithms is normally evaluated on standard benchmarks typically involving users fixating at points on the screen. However, these metrics do not easily translate into functional usability characteristics. In this work, we evaluate a state-of-the-art algorithm, FAZE, in a number of tasks of interest to the human-computer interaction community. Specifically, we study how gaze measured by FAZE could be used for dwell-based selection and reading progression (line identification and progression along a line) - key functionalities for users facing motor and visual impairments. We compared the gaze data quality from 7 participants using FAZE against that from an infrared tracker (Tobii Pro Spark). Our analysis highlights the usability of appearance-based gaze tracking for such applications.

Virtual reality as an engaging and enjoyable method for delivering emergency clinical simulation training: a prospective, interventional study of medical undergraduates

BackgroundIt is a requirement that medical students are educated in emergencies and feel well prepared for practice as a doctor, yet national surveys show that many students feel underprepared. Virtual reality (VR), combined with 360-degree filming, provides an immersive, realistic, and interactive simulation experience. Unlike conventional in-person simulation, it is scalable with reduced workforce demands. We sought to compare students’ engagement and enjoyment of VR simulation to desktop computer-based simulation.MethodsWe conducted a prospective, interventional, evaluation study. The study was carried out on final year medical students undertaking their Pre-Foundation Assistantship (n = 116) at Imperial College School of Medicine (ICSM) in London. We compared objective engagement, subjective engagement, and subjective enjoyment of VR simulation to desktop computer-based simulation using cardiac arrest and life-threatening asthma scenarios. Engagement was measured objectively using students’ physiological parameters, including heart rate and eye tracking, and facilitator observations using the validated ‘Behavioural Engagement Related to Instruction’ (BERI) protocol. Students’ subjective engagement and enjoyment levels were measured using a post-session survey.ResultsStudents’ maximum heart rates were significantly higher during VR simulation with a mean difference of 4.2 beats per minute (3.2 to 5.2, p < 0.001), and eye tracking showed they spent a significantly greater mean percentage of time of 6.4% (5.1 to 7.7, p < 0.001) focusing on the scenarios in VR compared to standard desktop. Qualitative data showed students enjoyed and felt engaged with the sessions, which provided a safe space for learning.ConclusionsOur study shows that students found VR simulations enjoyable and were more engaged compared to standard desktop simulation. This suggests that 360-degree VR simulation experiences provide students with immersive, realistic training, which is scalable, giving them the unique opportunity to manage emergencies and work within emergency teams, which would not typically occur during traditional training.

Deep learning–based eye tracking system to detect distracted driving

To investigate drivers’ gaze behavior and the characteristics of their gaze positions while driving, a natural driving behavior test method was employed alongside a non-contact eye-tracking device to conduct an in-vehicle experiment for collecting gaze data. Initially, we utilized the traditional approach to delineate the area of interest, analyzing variations in pupil diameter, gaze positions, and the duration spent in each area throughout the driving task, thereby compiling statistics on drivers’ gaze patterns. Subsequently, harnessing the You Only Look Once version 5 architecture, we can precisely identify the position of vehicles and obstacles from the captured images. Enhancements to the network model—including streamlining and integrating an attention mechanism—have significantly refined target detection accuracy. In the final analysis, by correlating drivers’ gaze data with the positional information of upcoming obstacles, we can accurately assess where drivers are looking. This fusion of data allows for a more nuanced observation of gaze dispersion and position within a one-second timeframe, providing valuable insights into drivers’ attention distribution and driving behaviors.

Classification of short and long term mild traumatic brain injury using computerized eye tracking

Accurate, and objective diagnosis of brain injury remains challenging. This study evaluated useability and reliability of computerized eye-tracker assessments (CEAs) designed to assess oculomotor function, visual attention/processing, and selective attention in recent mild traumatic brain injury (mTBI), persistent post-concussion syndrome (PPCS), and controls. Tests included egocentric localisation, fixation-stability, smooth-pursuit, saccades, Stroop, and the vestibulo-ocular reflex (VOR). Thirty-five healthy adults performed the CEA battery twice to assess useability and test–retest reliability. In separate experiments, CEA data from 55 healthy, 20 mTBI, and 40 PPCS adults were used to train a machine learning model to categorize participants into control, mTBI, or PPCS classes. Intraclass correlation coefficients demonstrated moderate (ICC > .50) to excellent (ICC > .98) reliability (p < .05) and satisfactory CEA compliance. Machine learning modelling categorizing participants into groups of control, mTBI, and PPCS performed reasonably (balanced accuracy control: 0.83, mTBI: 0.66, and PPCS: 0.76, AUC-ROC: 0.82). Key outcomes were the VOR (gaze stability), fixation (vertical error), and pursuit (total error, vertical gain, and number of saccades). The CEA battery was reliable and able to differentiate healthy, mTBI, and PPCS patients reasonably well. While promising, the diagnostic model accuracy should be improved with a larger training dataset before use in clinical environments.