Eye Movement Behavior Research Articles

Overcoming underload and overload: The effect of stage and level of autonomy in routine-failure trade-off in human-autonomy teams

We examined how human mental workload and the corresponding eye movement behaviors are affected by the stages and levels of autonomy in routine and autonomy failure conditions in human-autonomy teams (HAT). Thirty participants performed monitoring and diagnosing tasks with the autonomous agent in a three-factor experiment. The factors included information processing stage, level of autonomy, and agent operation condition. The results indicated that the later the agent-supported information processing stage or the higher the autonomy level, the higher the participants’ mental workload following autonomous agent failure. Compared to the continuous manual operation condition, the HAT performance did not decline following autonomous agent failure at the cost of increased mental workload. The eye movement results indicated a top-down compensatory control mechanism of attention, indicating the risk of team performance decline following autonomous agent failure. These findings can be applied in designing autonomous agents and setting human mental workload levels in a HAT.

Does the AR-HUD system affect driving behaviour? An eye-tracking experiment study

In recent years, the continuous development of in-vehicle information systems (IVIS) has greatly enriched the driving experience, while also occupying the driver’s cognitive resources to varying degrees, causing driving distraction. Therefore, studying the influence of the AR-HUD (Augmented Reality Head-up Display, AR-HUD) system on driving behaviour is of great significance for the in-vehicle information system to manage the complexity of information and enhance driving safety. This experiment applied five typical risky driving scenarios under two lighting conditions (daytime and night), as stimulus materials. The effects of eye movement behaviour and risk reaction time indicators during driving were comprehensively analysed. The experiment results showed that the AR-HUD system can significantly improve the subjects' attention to risky AOIs (Area of Interest, AOI) in night driving situations, as well as reduce the difficulty of processing information in risky driving scenarios, thus reducing cognitive load; In terms of reaction time, the AR-HUD system can significantly reduce the driver's perception time for risky driving scenarios, thus responding to risky situations more quickly. The experiment conclusions verified the role of AR-HUD technology in improving driving safety and driving behaviour, and provide a new direction for the future development of in-vehicle information systems.

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.

A dynamical method to objectively assess infantile nystagmus based on eye tracking. A pilot study

PurposeThe purpose of this research is to propose a new method for the easy, inexpensive and objective quantification of nystagmus using eye-tracking records collected during a simple reading task that could be implantable in clinical practice to assess patients with nystagmus. MethodsThis is a prospective, observational pilot study. Eye movements of 4 nystagmus patients and 9 healthy children during a reading task (a paragraph with 82 words) on a 15′’ monitor were collected and compared. Data are time series indicating the gaze position on the screen. Two quantifiers were proposed: IndS (based on the speed of movements) and IndF (based on the variation of the gaze trajectory). ResultsThe indices proposed reflect differences in the behavior of eye movements between the two groups. Nystagmus patients present higher values of IndS - indicating smaller number of slow movements (16% of movements with speeds <0.33 1/s for nystagmus and 85% for the control group, with p = 0.01) - and higher values of IndF - indicating higher gaze fluctuation (p = 0.01). Differences were not related with reading speed as show the mean and standard deviation: the nystagmus group required 115±45 s to complete the task and the control group 151±85 s; p = 0.73. ConclusionsThe proposed indices provide a new method that allows an objective assessment of nystagmus, with potential use in clinical and research practice to improve the follow-up of patients by monitoring the nystagmus over time or treatment.

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.

Association of AQP4 single nucleotide polymorphisms (rs335929 and rs2075575) with Parkinson’s disease: A case-control study

ObjectiveThe glymphatic system plays an important role in brain waste removal and is functionally and structurally dependent on astrocyte aquaporin-4 (AQP4). Genetic variation in the AQP4 gene has therefore been hypothesized to be associated with genetic susceptibility to neurodegenerative diseases. This study aimed to investigate whether two specific single nucleotide polymorphisms (SNP) in the AQP4 gene, rs335929, and rs2075575, are associated with the risk and clinical features of PD. MethodsA total of 950 participants, including 475 patients with sporadic PD and 475 independent healthy controls, were included in this case-control study. Two SNPs (rs335929 and rs2075575) of the AQP4 gene were genotyped using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Sanger sequencing was used to determine whether the genotyping results were accurate. A chi-square (χ2) test was used to compare the frequencies of alleles and genotypes between patients and controls. Logistic regression was used to calculate dominance ratios (OR) and 95% confidence intervals (CI). ResultsThe difference between rs2075575 in the dominant model (GG vs GA + AA: P = 0.019) and the overdominant model (GG + AA vs GA: P = 0.013) was statistically significant. Subgroup analysis showed that the frequency of the rs2075575 A allele was significantly higher in female PD patients than in matched female controls (P = 0.017). rs2075575 A allele was significantly more frequent in LOPD patients than in matched elderly controls (P = 0.033). rs335929 polymorphism was not significantly correlated with PD susceptibility in either the overall or subgroup analysis. Haplotype analysis between the two SNPs did not show an association with PD susceptibility. In addition, we found that the rs2075575 G allele was significantly associated with Rapid Eye Movement Behaviour Disorder (RBD) (P = 0.044), and the rs335929 A allele with memory impairment (P = 0.028) in PD. ConclusionThe AQP4 gene rs2075575 polymorphism may be associated with PD susceptibility, but not the rs335929 polymorphism. rs2075575 is associated with RBD and rs335929 is associated with memory cognition. Regulation of the glymphatic system by interfering with the genetics of AQP4 and thus influencing the pathology of PD may be a direction worth investigating. Studies in larger sample sizes and across ethnicities are essential for further understanding the potential association between AQP genes and PD pathogenesis.

Critically appraised topic on Rapid Eye Movement Behavior Disorder: From protein misfolding processes to clinical pathophysiology and conversion to neurodegenerative disorders

Critically appraised topic on Rapid Eye Movement Behavior Disorder: From protein misfolding processes to clinical pathophysiology and conversion to neurodegenerative disorders

The determinants of saccade targeting strategy in neurodevelopmental disorders: The influence of suboptimal reading experience

Whether eye-movements deficits are causal in reading disorders (RD) or rather a consequence of linguistic processing difficulty experienced by disabled readers has been extensively debated.Since RD are frequently comorbid with the Neurofibromatosis type1 (NF1), children with NF1 were used as a comparison group for children with dyslexia in this study.Eye movements were recorded while 21 dyslexic, 20 NF1, and 20 typically developing children performed an oculomotor lateralized bisection task. In this experiment, we manipulated the type of stimulus - discrete (words and strings of hashes) versus continuous (solid lines) - and the visual field where the stimulus was displayed (left vs right). The results showed that (1) only proficient readers (TD and NF1 without RD) showed fully developed oculomotor mechanisms for efficient reading, with a clear preferred viewing location located to the left of the word's centre in both visual fields, and fine-tuned saccade targeting guided by the between-character space information and (2) NF1 poor readers mirrored the dyslexic eye movement behaviour, with less accuracy and more variability in saccadic programming, no sensitivity to the discreteness of the stimuli, particularly in the left visual field. We concluded that disruption to oculomotor behaviour reflectsthe fact that many of the processes involved in reading are not yet automatized for children with RD, independently of NF1. This suggests that the differences in saccade targeting strategy between children with and without RD would be secondary consequences of their reduced reading experience.

Looking deep in the eyes: Investigating interpretation methods for neural models on reading tasks using human eye-movement behaviour

This paper provides the first broad overview of the relation between different interpretation methods and human eye-movement behaviour across different tasks and architectures. The interpretation methods of neural networks provide the information the machine considers important, while the human eye-gaze has been believed to be a proxy of the human cognitive process. Thus, comparing them explains machine behaviour in terms of human behaviour, leading to improvement in machine performance through minimising their difference. We consider three types of natural language processing (NLP) tasks: sentiment analysis, relation classification and question answering, and four interpretation methods based on: simple gradient, integrated gradient, input-perturbation and attention, and three architectures: LSTM, CNN and Transformer. We leverage two corpora annotated with eye-gaze information: the Zuco dataset and the MQA-RC dataset. This research sets up two research questions. First, we investigate whether the saliency (importance) of input-words conform with those from human eye-gaze features. To this end, we compute a saliency distance (SD) between input words (by an interpretation method) and an eye-gaze feature. SD is defined as the KL-divergence between the saliency distribution over input words and an eye-gaze feature. We found that the SD scores vary depending on the combinations of tasks, interpretation methods and architectures. Second, we investigate whether the models with good saliency conformity to human eye-gaze behaviour have better prediction performances. To this end, we propose a novel evaluation device called “SD-performance curve” (SDPC) which represents the cumulative model performance against the SD scores. SDPC enables us to analyse the underlying phenomena that were overlooked using only the macroscopic metrics, such as average SD scores and rank correlations, that are typically used in the past studies. We observe that the impact of good saliency conformity between humans and machines on task performance varies among the combinations of tasks, interpretation methods and architectures. Our findings should be considered when introducing eye-gaze information for model training to improve the model performance.

Eye movement behavior in a real-world virtual reality task reveals ADHD in children

Eye movements and other rich data obtained in virtual reality (VR) environments resembling situations where symptoms are manifested could help in the objective detection of various symptoms in clinical conditions. In the present study, 37 children with attention deficit hyperactivity disorder and 36 typically developing controls (9–13 y.o) played a lifelike prospective memory game using head-mounted display with inbuilt 90 Hz eye tracker. Eye movement patterns had prominent group differences, but they were dispersed across the full performance time rather than associated with specific events or stimulus features. A support vector machine classifier trained on eye movement data showed excellent discrimination ability with 0.92 area under curve, which was significantly higher than for task performance measures or for eye movements obtained in a visual search task. We demonstrated that a naturalistic VR task combined with eye tracking allows accurate prediction of attention deficits, paving the way for precision diagnostics.

User Experience Adaptation of Complex Game Interface for User Behaviour Modeling Using RNN

In a video game review, the main focus is the narratives, characters, graphics, and mechanics in the gameplay. Some recent research mentions the user interface only when it comes into light as a creative platform for simple interactive narratives from a technical point of view; this narrative is mainly a software tool that requires traditionally modernized inputs from the user. The user needs to interact with the navigational controls or menus in order to start a basic game play. A complex game interface as stimulus is generally considered as having a feeling of immersion that allows for visual tracking of user behavioural patterns and use it to predict the next strategy of the user using robust computational models. A number of users have limited sensory perception in a gameplay and hence rely on complex game stimulus and an adaptive model is paramount when considering behavioural expectations that place the user in a digital environment with more expressive perceptions. We developed a custom based eye tracking and 3D object detection algorithm which was utilised by recruiting users to interact with visual 3D objects and trace their eye movement behaviour to generated data. We then applied the use of recurrent neural network (RNN) for direct tracing of user behavioural activities in a sequential manner to predict their behaviour for interface adaptation. Result indicates that redundant user attributes are flexible and flawless for identifying predicted response of the user in a controlled environment. This would lead to prototypical representation of user behavioural analytics as an embedded platform in the confined digital environment. One of the limitations of the project is its inability to basically specify the 3D gaze point at the inner boundaries of the visual field. Data visualisation is strictly based on combined object flow detection. The originality of the work is its ability to redefine fixation point to a rendered cascaded 3D gaze point and space-defined saccade which is indicated by the distance between one gaze points to the other. The 3D gaze point would be well suited for fixation generalisation on 3D as well as on 2D digital oriented environment.

Impact of Cognitive Demand on Eye Movement Pattern in Patients with Alzheimer's Disease.

Eye-movement behavior has been used as a reliable tool to identify cognitive and behavioral patterns in individuals with different neuropsychiatric disorders including Alzheimer's disease (AD). Most studies in the field have been dedicated to evaluating eye-movement behavior during cognitive tasks in different protocols using multiple parameters. We aimed to evaluate the differences of eye-movement behavior in healthy subjects, subjects with mild cognitive impairment (MCI), and those with AD in a simple color task with and without cognitive demand. 91 subjects: 18 AD, 47 MCI, and 26 healthy controls had their oculomotor parameters assessed during baseline (no cognitive demand involved) and during a simple computational color memory task using an eye-tracker. Baseline showed statistically different and heterogeneous results between normal cognition and MCI groups. Familiarization phase of the task could not discriminate between groups in any of the analyzed parameters. AD subjects made longer fixations and visits on distractors, and more frequent fixations and visits on the target areas than other groups during the response phase. Eye-tracking time-related parameters differentiate AD subjects from other groups under cognitive demand even in a simple color memory task.

Actigraphy Enables Home Screening of Rapid Eye Movement Behavior Disorder in Parkinson's Disease.

Rapid eye movement sleep behavior disorder (RBD) is a potentially harmful, often overlooked sleep disorder affecting up to 70% of Parkinson's disease patients. Current diagnosis relies on nocturnal video-polysomnography, which is an expensive and cumbersome examination requiring specific clinical expertise. Here, we explored the use of wrist actigraphy to enable automatic RBD diagnoses in home settings. A total of 26 Parkinson's disease patients underwent 2-week home wrist actigraphy, followed by two in-laboratory evaluations. Patients were classified as RBD versus non-RBD based on dream enactment history and video-polysomnography. We comprehensively characterized patients' movement patterns during sleep using actigraphic signals. We then trained machine learning classification algorithms to discriminate patients with or without RBD using the most relevant features. Classification performance was quantified with respect to clinical diagnosis, separately for in-laboratory and at-home recordings. Performance was further validated in a control group of non-Parkinson's disease patients with other sleep conditions. To characterize RBD, actigraphic features extracted from both (1) individual movement episodes and (2) global nocturnal activity were critical. RBD patients were more active overall, and showed movements that were shorter, of higher magnitude, and more scattered in time. Using these features, our classification algorithms reached an accuracy of 92.9 ± 8.16% during in-clinic tests. When validated on home recordings in Parkinson's disease patients, accuracy reached 100% over a 2-week window, and was 94.4% in non-Parkinson's disease control patients. Features showed robustness across tests and conditions. These results open new perspectives for faster, cheaper, and more regular screening of sleep disorders, both for routine clinical practice and clinical trials. ANN NEUROL 2023;93:317-329.

Biosensors toward behavior detection in diagnosis of alzheimer's disease.

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer's disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

Detection of insomnia using advanced complexity and entropy features of sleep stage data of EEG recordings

Sleep disorders affect the physical ability in the function under mental, emotional, and physical forms depending upon its intensity. These abnormalities are observed on the basis of structure of sleep. Most prevalent sleep disorders are insomnia, bruxism, depression, and narcolepsy. Common issues in sleep disorder are sleeplessness, irregular leg movements, problems with fast eye movement behavior and breathing abnormalities. Therefore, an early-stage therapy that might save a patient's life depends on a precise diagnosis and categorization. The much more sensitive as well as significant bio-signal is electroencephalographic (EEG) signal. It has capacity to record sleep-sensitive brain activity. We used an available EEG database which had recordings divided into different types of sleep disturbances as well as a healthy control group. Popular sensor's EEG brain function has been examined. Ultimately, using patterns taken from EEG data, a categorization AI model was created. Extracted characteristics worked well as a biomarker for identifying sleep problems when combined with an AI classifier.

Stable Working Memory and Perceptual Representations in Macaque Lateral Prefrontal Cortex during Naturalistic Vision.

Primates use perceptual and mnemonic visuospatial representations to perform everyday functions. Neurons in the lateral prefrontal cortex (LPFC) have been shown to encode both of these representations during tasks where eye movements are strictly controlled and visual stimuli are reduced in complexity. This raises the question of whether perceptual and mnemonic representations encoded by LPFC neurons remain robust during naturalistic vision-in the presence of a rich visual scenery and during eye movements. Here we investigate this issue by training macaque monkeys to perform working memory and perception tasks in a visually complex virtual environment that requires navigation using a joystick and allows for free visual exploration of the scene. We recorded the activity of 3950 neurons in the LPFC (areas 8a and 9/46) of two male rhesus macaques using multielectrode arrays, and measured eye movements using video tracking. We found that navigation trajectories to target locations and eye movement behavior differed between the perception and working memory tasks, suggesting that animals used different behavioral strategies. Single neurons were tuned to target location during cue encoding and working memory delay, and neural ensemble activity was predictive of the behavior of the animals. Neural decoding of the target location was stable throughout the working memory delay epoch. However, neural representations of similar target locations differed between the working memory and perception tasks. These findings indicate that during naturalistic vision, LPFC neurons maintain robust and distinct neural codes for mnemonic and perceptual visuospatial representations.SIGNIFICANCE STATEMENT We show that lateral prefrontal cortex neurons encode working memory and perceptual representations during a naturalistic task set in a virtual environment. We show that despite eye movement and complex visual input, neurons maintain robust working memory representations of space, which are distinct from neuronal representations for perception. We further provide novel insight into the use of virtual environments to construct behavioral tasks for electrophysiological experiments.

Derivation and Validation of a Phenoconversion-Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder.

Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α-synucleinopathies. Reliable biomarkers are needed to predict phenoconversion. The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM-PCA]). Seventy-six consecutive iRBD patients (70 ± 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 ± 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow-up time: 21 ± 14months; 46 nonconverters, 69 ± 6 years, follow-up time: 33 ± 19months). All patients underwent [18 F]FDG-PET (18 F-fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM-PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power. First, we derived and validated two distinct center-specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM-PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6-21.4). We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [18 F]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Sleep disturbances in Lewy body dementia: A systematic review.

Lewy body dementia (LBD) refers to both dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Sleep disturbances are common in LBD, and can include poor sleep quality, excessive daytime sleepiness (EDS), and rapid eye movement behaviour disorder (RBD). Despite the high clinical prevalence of sleep disturbances in LBD, they are under-studied relative to other dementias. The aim of the present systematic review was to examine the nature of sleep disturbances in LBD, summarise the effect of treatment studies upon sleep, and highlight specific and necessary directions for future research. Published studies in English were located by searching PubMED and PSYCArticles databases (until 10 June 2022). The search protocol was pre-registered in PROSPERO (CRD42021293490) and performed in accordance with PRISMA guidelines. Following full-text review, a final total of 70 articles were included. These included 20 studies focussing on subjective sleep, 14 on RBD, 8 on EDS, 7 on objective sleep, and 1 on circadian rhythms. The majority of the 18 treatment studies used pharmacological interventions (n=12), had an open-label design (n=8), and were of low-to-moderate quality. Most studies (n=55) included only patients with DLB. Due to the heterogeneity of the studies, we reported a narrative synthesis without meta-analysis. At least one form of sleep disturbance may be present in as many as 90% of people with LBD. Subjectively poor sleep quality, excessive daytime sleepiness, and RBD are more common and severe in LBD relative to other dementias.

Supervised approach based sleep disorder detection using non - Linear dynamic features (NLDF) of EEG

Depending on its intensity, sleep disorders can affect a person's ability to function mentally, emotionally, and physically. These are medical abnormalities of the subject's sleep structure. Most prevalent ones are bruxism, sleep problems, depression, and narcolepsy. A greater chance of acquiring sleep issues in the elderly includes sleeplessness, irregular leg movements, problems with fast eye movement behaviour and breathing abnormalities. Therefore, an early stage therapy that might save a patient's life depends on a precise diagnosis and categorization. The much more sensitive as well as significant bio-signal is electroencephalographic (EEG) signal. It has the capacity to record sleep-sensitive brain activity. We used an available EEG database which had recordings divided into different types of sleep disturbances as well as a healthy control group. Popular sensor's EEG brain function has been examined. Ultimately, using patterns taken from EEG data, a categorization AI model was created. Extracted characteristics worked well as a biomarker for identifying sleep problems when combined with an AI classifier.

Analysis of Characteristic Eye Movement Behaviors of Young Adults with Different Degrees of Myopia

To measure the parameters of eye movement of young adult patients of myopia, to compare the differences in the parameters of patient groups with varied degrees of myopia, and to analyze the correlation between eye movement parameters and axial length (AL) and spherical equivalent refraction (SER). A total of 91 young adult patients of myopia were recruited. The subjects were divided into three groups of low, moderate, and high myopia according to their SER. Information on the subjects' age and sex was collected and general clinical examination was completed. The subjects' binocular fixation, reflexive saccade, and antisaccade were measured with eye tracker. One-way ANOVA or Kruskal-wallis test was used to compare the general data and eye movement parameters of the three groups of myopic patients. Pearson or Spearman correlation analysis was used to analyze AL and SER's correlation with 95% bivariate contour ellipse area (BCEA) and saccadic parameters. The 95% BCEA for mild, moderate, and high myopia groups were 2.08 (0.54, 14.69) deg 2, 4.99 (0.94, 49.22) deg 2, and 5.79 (2.18, 64.91) deg 2, respectively. There was significant difference between the 95% BCEA of the three groups ( P=0.029). The 95% BCEA of the mild myopia group was significantly smaller than that of the high myopia group ( P=0.01). There were no significant differences in saccadic parameters among the three groups ( P>0.05). There was a significant positive correlation between 95% BCEA and AL ( r=0.342, P=0.001). There was a significant negative correlation between 95% BCEA and SER ( r=-0.322, P=0.002). There was no significant correlation between the parameters of saccadic movement and the AL or SER ( P>0.05). For young adult myopic patients, the longer the AL is, the higher the degree of the myopia and the worse the fixation stability becomes. The fixation stability of patients with high myopia was significantly worse than that of patients with mild myopia. There was no significant difference in saccadic parameters in patients with different degrees of myopia.