Eye Movement Analysis Research Articles

Quantification of Oculomotor Responses and Accommodation through Instrumentation and Analysis Toolboxes.

Through the purposeful stimulation and recording of eye movements, the fundamental characteristics of the underlying neural mechanisms of eye movements can be observed. VisualEyes2020 (VE2020) was developed based on the lack of customizable software-based visual stimulation available for researchers that does not rely on motors or actuators within a traditional haploscope. This new instrument and methodology have been developed for a novel haploscope configuration utilizing both eye tracking and autorefractor systems. Analysis software that enables the synchronized analysis of eye movement and accommodative responses provides vision researchers and clinicians with a reproducible environment and shareable tool. The Vision and Neural Engineering Laboratory's (VNEL) Eye Movement Analysis Program (VEMAP) was established to process recordings produced by VE2020's eye trackers, while the Accommodative Movement Analysis Program (AMAP) was created to process the recording outputs from the corresponding autorefractor system. The VNEL studies three primary stimuli: accommodation (blur-driven changes in the convexity of the intraocular lens), vergence (inward, convergent rotation and outward, divergent rotation of the eyes), and saccades (conjugate eye movements). The VEMAP and AMAP utilize similar data flow processes, manual operator interactions, and interventions where necessary; however, these analysis platforms advance the establishment of an objective software suite that minimizes operator reliance. The utility of a graphical interface and its corresponding algorithms allow for a broad range of visual experiments to be conducted with minimal required prior coding experience from its operator(s).

Threshold Determination Method for Detecting the Gazing Areas on a PC Screen While Silent Reading Japanese Texts

This study aims to develop a method that can detect the areas of Japanese texts that are difficult to read on a PC screen by analyzing eye movements. An effective method for detecting the areas of text that are difficult to read is to create a heatmap and consider the areas where the eye stays for longer than a fixation duration threshold (threshold). Usually, the threshold is determined through a 50 ms step search. However, since the eye stay time (fixation duration) for difficult‐to‐read text is longer than the fixation duration when reading smoothly, it is expected that steps that last longer than 50 ms are more effective at detecting the threshold. Furthermore, there are individual differences in the speed at which we comprehend text. In this study, we proposed a method that considers both the efficient search and individual differences among each subject. The experimental results showed that the threshold determined by the proposed method is more useful than the thresholds of 1000 and 800 ms that were used in a previous study, as well as the usual fixation duration of 250 ms. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

AN IMPROVED DYNAMIC TIME WARPING METHOD COMBINING DISTANCE DENSITY CLUSTERING FOR EYE MOVEMENT ANALYSIS

Analyzing eye movement data to evaluate learning status has become crucial in intelligent education. The eye movement scanning path can directly or indirectly reflect changes in thinking patterns and psychological states. By analyzing the scanning path, we can explore the commonality and differences in learners’ eye movement behaviors and provide essential references for improving visual content and giving guidance. This paper first studies the time series representation and clustering of the learner’s scanning path under the same task. Then, the three learning states of concentration, mind-wandering, and information wandering are evaluated through the clustering results. Specifically, the improved DBA algorithm (iDBA) is proposed to extract group eye movement patterns, combined with the dynamic time warping (DTW) algorithm to calculate the similarity of scanning paths and determine the clustering seeds, while the distance density clustering (DDC) algorithm is used for clustering. Experiments show that time series-based eye movement pattern mining can identify group viewing behaviors. Meanwhile, clustering reveals different reading strategies and provides the ability to assess learning status.

Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders.

Experienced psychiatrists identify people with autism spectrum disorder (ASD) and schizophrenia (Sz) through interviews based on diagnostic criteria, their responses, and various neuropsychological tests. To improve the clinical diagnosis of neurodevelopmental disorders such as ASD and Sz, the discovery of disorder-specific biomarkers and behavioral indicators with sufficient sensitivity is important. In recent years, studies have been conducted using machine learning to make more accurate predictions. Among various indicators, eye movement, which can be easily obtained, has attracted much attention and various studies have been conducted for ASD and Sz. Eye movement specificity during facial expression recognition has been studied extensively in the past, but modeling taking into account differences in specificity among facial expressions has not been conducted. In this paper, we propose a method to detect ASD or Sz from eye movement during the Facial Emotion Identification Test (FEIT) while considering differences in eye movement due to the facial expressions presented. We also confirm that weighting using the differences improves classification accuracy. Our data set sample consisted of 15 adults with ASD and Sz, 16 controls, and 15 children with ASD and 17 controls. Random forest was used to weight each test and classify the participants as control, ASD, or Sz. The most successful approach used heat maps and convolutional neural networks (CNN) for eye retention. This method classified Sz in adults with 64.5% accuracy, ASD in adults with up to 71.0% accuracy, and ASD in children with 66.7% accuracy. Classifying of ASD result was significantly different (p<.05) by the binomial test with chance rate. The results show a 10% and 16.7% improvement in accuracy, respectively, compared to a model that does not take facial expressions into account. In ASD, this indicates that modeling is effective, which weights the output of each image.

Comparative analysis of light environment perception, eye movement and physiology in university professional classroom based on virtual reality experiment

In this study, virtual reality technology was used to simulate the virtual scene of three common light environment forms of professional classrooms in universities. The experimental results show that people’s environmental perception is related to the average pupil diameter, saccade frequency, high frequency/low frequency (LF/HF) and skin conductance change rate in a combined light environment; moreover, when the colour temperature is 4500 K, the space is brighter, more spacious, more relaxed and the willingness to learn is stronger. In the range of 22.4°C–25°C, temperature does not affect people’s perception of the warm feeling of the light environment, while the light colour temperature has a greater impact on people’s perception of the warm feeling. The lower the light colour temperature is, the warmer people feel subjectively. However, people’s eye movement behaviour and physiological system data have no obvious perception of colour temperature changes. Indoor noise affects the indoor brightness perception to some extent. In the range of 43–46.9 dB, with the increase of noise value, the evaluation score of people’s brightness perception basically increases. In terms of learning psychological state, the more comfortable the light environment is, the stronger the willingness to learn and the higher the learning enthusiasm.

Classification of MCI patients using vergence eye movements and pupil responses obtained during a visual oddball test

In the current study, we tested the hypothesis that Mild Cognitive Impairment (MCI) patients can be identified based on the analysis of vergence eye movements and pupil responses. We recorded vergence and pupil responses in MCI patients (N = 22) and cognitive healthy elderly (N = 18) while performing a visual oddball task. Based on selected features, a classifier model computed probability scores predicting MCI. MCI patients were re-evaluated in a follow-up visit of 12–18 months. For validating the model, patients with Alzheimer's Disease (AD) (N = 9) were tested. High classification accuracy was obtained (AUC: 0.93). In addition, the probability scores showed significant predictive power of MCI conversion into possible AD. Our results show that MCI can be detected by assessing vergence and pupil responses during a simple and short task. Therefore, these responses could potentially be used as a marker tool for MCI diagnosis and to identify the risk of developing Alzheimer's Disease.

Using Gaze for Behavioural Biometrics.

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.

Training With Simulated Scotoma Leads to Behavioral Improvements Through at Least Two Distinct Mechanisms.

Individuals with central vision loss due to macular degeneration (MD) often spontaneously develop a preferred retinal locus (PRL) outside the area of retinal damage, which they use instead of the fovea. Those who develop a stable PRL are more successful at coping with their vision loss. However, it is unclear whether improvements in visual performance at the PRL are specific to that retinal location or are also observed in other parts of the retina. Perceptual learning literature suggests that the retinal specificity of these effects provides insight about the mechanisms involved. Better understanding of these mechanisms is necessary for the next generation of interventions and improved patient outcomes. To address this, we trained participants with healthy vision to develop a trained retinal locus (TRL), analogous to the PRL in patients. We trained 24 participants on a visual search task using a gaze-contingent display to simulate a central scotoma. Results showed retinotopically specific improvements in visual crowding only at the TRL; however, visual acuity improved in both the TRL and in an untrained retinal locus. These results suggest that training with an artificial scotoma involves multiple mechanistic levels, some location-specific and some not, and that simulated scotoma training paradigms likely influence multiple mechanisms simultaneously. Eye movement analysis suggests that the non-retinotopic learning effects may be related to improvements in the capability to maintain a stable gaze during stimulus presentation. This work suggests that effective interventions promoting peripheral viewing may influence multiple mechanisms simultaneously.

Correction: Comparing the Reading Behaviours of Students with High- and Low-Level Comprehension of Scientific Terms by Eye Movement Analysis

Correction: Comparing the Reading Behaviours of Students with High- and Low-Level Comprehension of Scientific Terms by Eye Movement Analysis

How scene encoding affects memory discrimination: Analysing eye movements data using data driven methods

ABSTRACT Encoding of visual scenes remains under-explored due to methodological limitations. In this study, we evaluated the relationship between memory accuracy for visual scenes and eye movements at encoding. First, we used data-driven methods, a fixation density map (using iMap4) and a saliency map (using GBVS), to analyse the visual attention for items. Second, and in a more novel way, we conducted scanpath analyses without a priori (using ScanMatch). Scene memory accuracy was assessed by asking participants to discriminate identical scenes (targets) among rearranged scenes sharing some items with targets (distractors) and new scenes. Shorter fixation duration in regions of interest (ROIs) at encoding was associated with a better rejection of distractors; there was no significant difference in the relative fixation time in ROIs at encoding, between subsequent hits and misses at test. Hence, density of eye fixations in data-driven ROIs seems to be a marker of subsequent memory discrimination and pattern separation. Interestingly, we also identified a negative correlation between average MultiDimensional Scaling (MDS) distance scanpaths and the correct rejection of distractors, indicating that scanpath consistency significantly affects the ability to discriminate distractors from targets. These data suggest that visual exploration at encoding participates in discrimination processes at test.

Analysis of Eye Movement during Insurance Decision Making Correlation between Eye Movement and Psychometric Questionnaire

Analysis of Eye Movement during Insurance Decision Making Correlation between Eye Movement and Psychometric Questionnaire

Effect of Perspective on Eye Movement of Aesthetic Preference Research on Wai Shape of New Chinese Style Arhat Bed

The new chinese style Arhat bed is taken as the research objective to explore the effect of people’s view on people’s aesthetic preferences for Wai shape of new chinese Arhat bed, using the eye movement analysis method through comprehensive analysis on eye movement data from volunteers. Eye movements characteristics of 20 volunteers in watching pictures of different new chinese Arhat bed Wai shape are recorded by SR research Eyelink head mounted eye tracker. Experimental results show that eye movement data of men and women are in the same trend, but there are few data in instability and the male and female have a significant correlation with the time and the number of fixation points, with a obvious regression effect, under two different presented Perspective of products. In all 16 samples selected, samples 12 has the most fixation and fixation times, while samples 15 and samples 13 has the least. Visual fatigue and the difference between 3D model and the real Arhat bed products have some effects on the experimental data. It makes no difference for people to choose products from different perspectives in where the products are placed, and people prefer the Wai shape which contains column and is simple.

A New Approach for Lie Detection Using Non-Linear and Dynamic Analysis of Video-Based Eye Movement

Purpose: This study aimed to evaluate a lie-detection system by non-linear analysis of video-based eye movement.&#x0D; Materials and Methods: The physiological signals, as well as video-based eye movement in horizontal and vertical channels, were recorded based on a Control Question Test (CQT). The dynamics of eye movement signals were then analyzed by Recurrence Quantification Analysis (RQA). Statistical analysis was performed by ANOVA and Linear Discriminate Analysis (LDA).&#x0D; Results: In this study, 40 subjects participated. The statistical analysis results of vertical eye movement indicated that ENT measures increased significantly for relevant questions in comparison to other questions. Moreover, a significant increase was observed in all RQA parameters except Lmax and DET for horizontal eye movement. The results of LDA using psychophysiology features. The accuracy percentage of 78.4% and 81.86% were obtained for lie detection using physiological signals and optimal RQA parameters of video-based eye movements, respectively.&#x0D; Conclusion: The accuracy of lie detection by significant RQA parameters was more than the accuracy of physiological signals. So, the results of this study illustrate that the dynamic technique is well suited to analyze eye movement signals under stress and it could be recommended as a useful method in lie detection.

Analysis of Eye Movements with 2D Images Obtained by EOG Signals

Analysis of Eye Movements with 2D Images Obtained by EOG Signals

Role of Pitch in Toddler Looking to New and Given Referents in American English

ABSTRACT This study examined how toddler looking to a discourse referent is mediated by the information status of the referent and the pitch contour of the referring expression. Eighteen-month-olds saw a short discourse of three sets of images with the proportion of looking time to a target analyzed during the final image. At test, the information status of the referent was either new or given and the referring expression was presented with one of three pitch contours (flat f0, monotonal (~H*), or bitonal (~L+H*)). In Experiment 1, toddlers looked reliably longer to a target referent when it was either new to the discourse or carried a non-flat pitch contour. In Experiment 2, the referring expression was removed to observe effects of information status alone on looking to a target referent. Toddlers looked significantly longer to a target when it was new versus given. More fine-grained time course analyses of eye movements revealed differences in the speed and duration of fixation to a target. Overall, the experiments show that discourse reference in toddlers is mediated by the presence of newness and pitch contours, even in the case of given information.

The “eye” in imagination: The role of eye movements in a reciprocal aiming task

Humans not only perform a variety of actions, but they also simulate or imagine themselves performing those actions. When individuals physically execute goal-directed hand movements, eye movements typically precede the hand movements to the target to enhance movement accuracy. Studies have also revealed that eye movements emerge during motor imagery. Although eye-hand coordination is clearly important for the execution of a goal-directed movement, less is known about the role or expression of eye movements in an imagined movement. The present experiments were designed to investigate the role of eye movements during an executed and imagined reciprocal aiming task. Participants executed and imagined reciprocal aiming movements under conditions in which they were allowed to freely move their eyes or were told to fixate at a fixation point. Speed-accuracy trade-offs consistent with Fitts’ Law were observed across all conditions suggesting that eye movements were not necessary to execute or imagine movements. Movement times were longest, however, in the imagination task when the eye movements were restricted to the central fixation point, suggesting that eye movements might assist with the accuracy or calibration of the imagination process. Analysis of eye movements during the no fixation imagination task revealed that the eye movements during imagination mimicked the executed hand movements when gaze was not restricted. Overall, these results suggest that although the ability to make eye movements was not necessary for action execution or motor imagery, the use of eye movements likely enhancing the accuracy of motor imagery for this task.

Eye Movement Analysis and Usability Assessment on Affective Computing Combined with Intelligent Tutoring System

Education is the key to achieving sustainable development goals in the future, and quality education is the basis for improving the quality of human life and achieving sustainable development. In addition to quality education, emotions are an important factor to knowledge acquisition and skill training. Affective computing makes computers more humane and intelligent, and good emotional performance can create successful learning. In this study, affective computing is combined with an intelligent tutoring system to achieve relevant and effective learning results through affective intelligent learning. The system aims to change negative emotions into positive ones of learning to improve students’ interest in learning. With a total of 30 participants, this study adopts quantitative research design to explore the learning situations. We adopt the System Usability Scale (SUS) to evaluate overall availability of the system and use the Scan Path to explore if the subject stays longer in learning the course. This study found that both availability and satisfaction of affective tutoring system are high. The emotional feedback mechanism of the system can help users in transforming negative emotions into positive ones. In addition, the system is able to increase the learning duration the user spends on learning the course as well.

A machine learning approach to primacy-peak-recency effect-based satisfaction prediction

Data retrieval systems supporting the discovery and reuse of open data are emerging as important tools in the open data ecosystem. However, user satisfaction with them is relatively low. This study proposes the primacy-peak-recency effect to investigate the cognitive mechanisms underlying data searchers’ overall satisfaction. To test the primacy-peak-recency effect, primacy-peak-recency cubes consisting of eye movement indicators at primacy, peak, and recency moments and their combinations are constructed as the theoretical model. A user experiment was conducted to collect eye movement data and satisfaction scores generated during 48 doctoral students’ interactions with data retrieval systems. An ensemble machine learning framework was then applied to analyze eye movement data to assess the theoretical model. The results indicate that the primacy-peak-recency cubes are salient predictors of data searchers’ satisfaction (the prediction accuracy=0.682 and regression R2=0.749). This finding suggests that data searchers’ complex cognitive processes at primacy, peak, and recency moments measured by uni-, bi-, and three-dimensional eye movement indicators are predictors of overall satisfaction, confirming the primacy-peak-recency effect. In addition, combinations of varying types of influential moments and multidimensional eye movement events are the best predictors of overall satisfaction. This suggests that influential moments and cognitive processes have additive effects on overall satisfaction. Combining theory-driven and data-driven approaches, this study sheds light on the potential of machine learning approaches for analyzing neuropsychological data for heuristics examination. With these insights, practical strategies to predict data searchers’ satisfaction and optimize the user-experience design of data retrieval systems are proposed.

Visual Attention during Scene Viewing – Eye Tracking Discovery with K-Means and Gaussian Mixture Model

Nature scenes can generate cognitive (Berto, 2005) and affective (Bratman et al., 2015) benefits. Based on the Attention Restoration Theory (Kaplan, 1995, 2001), nature scenes attract attention effortlessly and allow the replenishment of directed attention. Researchers have tested the hypothesis that nature and urban scenes differ in visual processing, and found supporting evidence in basic eye movement parameters (e.g., fixation, saccade; Berto et al., 2008; Valtchanov & Ellard, 2015). The current investigation sought to apply k-means and Gaussian Mixture Models (GMM) to test this hypothesis and capture the spatial relationship of visual attention beyond basic eye movement parameters. Participants free viewed natural and urban scenes for five seconds. Free viewing could be followed by an attention check where participants reported which of the four scenes they had just viewed. Data from experiment 1 (n = 158) was first analyzed, followed by experiment 2 (n = 45) to confirm the results of experiment 1. Kmeans clustering was carried out using the KMeans function in the scikit-learn Python library. GMM analysis was carried out using the Eye Movement analysis with Hidden Markov Models (EMHMM) toolbox (Chuk et al., 2014). Paired t-tests revealed consistent findings between the experiments. Specifically, in comparison with urban scences, nature had fewer fixations, longer fixaiton durations, smaller saccade amplitudes, and fewer clusters in both kmeans and GMM models (p < .05). Additional analyses on Calinski Harabasz scores of kmeans models revealed lower scores in nature scenes. Consistent with previous literature (Berto et al., 2008; Valtchanov & Ellard, 2015), the visual processing of nature and urban scences do differ in fixations and saccades. We further confirmed that kmeans and GMM are capable of capturing this difference. Additional research is needed to compare kmeans and GMM in their abilities to assess the differences between nature and urban visual processing.

An ex-Gaussian analysis of eye movements in L2 reading

AbstractSecond language learners’ reading is less efficient and more effortful than native reading. However, the source of their difficulty is unclear; L2 readers might struggle with reading in a different orthography, or they might have difficulty with later stages of linguistic interpretation of the input, or both. The present study explored the source of L2 reading difficulty by analyzing the distribution of fixation durations in reading. In three studies, we observed that L2 readers experience an increase in Mu, which we interpret as indicating early orthographic processing difficulty, when the L2 has a significantly different writing system than the L1 (e.g., Chinese and English) but not when the writing systems were similar (e.g., Portuguese and English). L2 readers also experienced an increase in Tau, indicating later-arising processing difficulty which likely reflects later-stage linguistic processes, when they read for comprehension. L2 readers of Chinese also experienced an additional increase in Tau.