Oculomotor behavior and perceptual strategies in complex tasks

Color fusion and rivalry are two key information integration mechanisms in binocular vision, representing the visual system's processing patterns for consistent and conflicting inputs, respectively. This study hypothesizes that there are quantifiable differences in eye movement indicators under states of binocular color fusion and rivalry, which can be verified through multi-paradigm eye movement experiments. The experiment recruited eighteen subjects with normal vision (nine males and nine females), employing the Gaze Stability paradigm, Straight Curve Eye Hopping paradigm, and Smoothed Eye Movement Tracking paradigm for eye movement tracking. Each paradigm included a binocular color rivalry experimental group (R-G) and two binocular color fusion control groups (R-R, G-G). Data analysis indicates significant differences in indicators such as Average Saccade Amplitude, Median Saccade Amplitude, and SD of Saccade Amplitude between binocular color fusion and rivalry states. For instance, through Z-Score normalization and cross-paradigm merged analysis, specific ranges of these indicators were identified to distinguish between the two states. When the Average Saccade Amplitude falls within the range of -0.905--0.693, it indicates a state of binocular color rivalry; when the range is 0.608-1.294, it reflects a state of binocular color fusion. Subsequently, ROC curve analysis confirmed the effectiveness of the experimental paradigms in analyzing the mechanisms of binocular color fusion and rivalry, with AUC values of 0.990, 0.741, and 0.967, respectively. These results reveal the potential of eye movement behaviors as biomarkers for the dynamic processing of visual conflicts. This finding provides empirical support for understanding the neural computational models of binocular vision and lays a methodological foundation for developing visual impairment assessment tools based on eye movement features.

In this paper, we call the point on an object where human looks a subjective viewpoint and we call the point where the object and the straight line going through the center of pupil and the center of the fovea crosses an objective viewpoint. We realize eye movements in reading on the computer when an objective viewpoint is a subjective viewpoint and when an objective viewpoint is shifted to the right of a subjective viewpoint and we investigate the characteristics of eye movements. First, we realize a computer-simulation of human eye movements in reading. Secondly, the superiority of the right visual field appears in the foveated vision realized by shifting an objective viewpoint to the right of a subjective viewpoint. And we confirm that the gap between the subjective viewpoint and the objective viewpoint is important by comparing the characteristics of human eye movements to the characteristics of eye movements when an objective viewpoint is shifted to the right of a subjective viewpoint. Furthermore, we perform eye movements on the computer while shifting an objective viewpoint to the right of a subjective viewpoint and measure the speed at which an English sentence is read. We conclude that reading speed when the foveated vision has the superiority of the right visual field is faster than that when the foveated vision doesn't have it.

The newest generation of confocal scanning laser ophthalmoscopes with adaptive optics correction of ocular aberrations provides retinal images of unprecedented resolution, allowing for real-time imaging of photoreceptors in the living human eye. Natural fixational eye movements made by the subject/patient during recording produce distortions that are unique in each frame. Correction for these distortions is necessary before multiple frames can be added together to achieve noise reduction or to build a mosaic image from different retinal areas. Here we describe the characteristics of fixational eye movements and the distortions they produce during retinal imaging, we show examples of images with particular distortions, and show eye movement records obtained during the correction of these distortions.

The study of the saccadic system has focused mainly on neurons active before the beginning of saccades, in order to determine their contribution in movement planning and execution. However, most oculomotor structures contain also neurons whose activity starts only after the onset of saccades, the maximum of their activity sometimes occurring near saccade end. Their characteristics are still largely unknown. We investigated pretectal neurons with saccade-related activity in the alert cat during eye movements towards a moving target. They emitted a high-frequency burst of action potentials after the onset of saccades, irrespective of their direction, and will be referred to as "pretectal saccade-related neurons". The delay between saccade onset and cell activity varied from 17 to 66 ms on average. We found that burst parameters were correlated with the parameters of saccades; the peak eye velocity was correlated with the peak of the spike density function, the saccade amplitude with the number of spikes in the burst, and burst duration increased with saccade duration. The activity of six pretectal saccade-related neurons was studied during smooth pursuit at different velocities. A correlation was found between smooth pursuit velocity and mean firing rate. A minority of these neurons (2/6) were also visually responsive. Their visual activity was proportional to the difference between eye and target velocity during smooth pursuit (retinal slip). These results indicate that the activity of pretectal saccade-related neurons is correlated with the characteristics of eye movements. This finding is in agreement with the known anatomical projections from premotor regions of the saccadic system to the pretectum.

Vision is one of the most important human senses, accounting for most of the external information pedestrians receive while crossing the street. However, distracted mobile phone usage during street crossing consumes pedestrians’ cognitive resources and diverts their visual attention. As a result, pedestrians may be unable to fully concentrate on observing the traffic environment and effectively planning their crossing path and behavior. This study evaluated the effect of pedestrian behavioral activities at street crossings on eye-movement (EM) characteristics. The crossing tasks were natural behavior, voice call, text messaging, and listening to music. The tasks were further categorized as simple or complex. A total of 29 participants were recruited in Nanjing: 18 males (62.1%) and 11 females (37.9%) with an average age of 23.59 years (SD = 2.44). The Friedman test was used to analyze differences in saccade frequency, fixation time, browsing number, and browsing time across different scenarios. Text messaging had the most significant impact on pedestrians’ EM characteristics, followed by voice call; music listening had a relatively weaker effect. Secondary task difficulty influenced the percentage of browsing, viewing, and to some extent gaze time. On the other hand, music rhythm and style only partially influenced the percentage of gaze and gaze time. Mobile phones substantially affected pedestrians’ EM characteristics and attention allocation for the same level of secondary task difficulty. These findings contribute to a better understanding of pedestrians’ visual characteristics under distracted mobile phone usage conditions and provide valuable insights for developing appropriate measures to enhance pedestrian safety.

Mean fixation duration and mean saccade amplitude during active viewing tasks differ from person to person. Previous studies have shown that these individual differences tend to be stable across at least some tasks, suggesting that they may reflect underlying traits associated with individuals. However, whether these individual differences are also stable over time has not been established. The present study established stable individual differences in mean fixation duration and mean saccade amplitude across 4 viewing tasks, showed that the observed individual differences are stable over several days, and extended these results to standard deviations of fixation duration and saccade amplitude. The results have implications for theories of eye movement control and for using eye movement characteristics as individual difference measures.

BackgroundEye movement is a vital indicator of neurodegenerative diseases, brain health, and behavior. However, existing knowledge is limited to patient populations or cross-sectional samples. Little is known about eye movement in association with longitudinal cognitive and mobility decline in aging.ObjectiveInvestigate relationships between eye movement features with cognitive impairment, including Alzheimer's disease (AD), and longitudinal decline in cognition and mobility.MethodsIn 543 Baltimore Longitudinal Study of Aging participants (mean age = 71 years), we examined associations of eye movements with cognitive impairment of any severity, vascular conditions, and falls using logistic regression, and up to 18-year longitudinal changes in cognition and mobility using linear mixed-effects models. Four eye movement features (saccade, smooth pursuit, vergence, optokinetic nystagmus) were derived from a portable eye-tracking perimeter (Neurolign Dx100) using machine learning Least Absolute Shrinkage Selection Operator regression.ResultsHigher saccade, smooth pursuit, and vergence were bivariately or marginally associated with lower odds of cognitive impairment, including AD, and vascular diseases. In age- and sex-adjusted models, higher saccade was associated with slower declines in cognition (attention: Trail Making Test-Part A), mobility, and balance. Higher smooth pursuit was associated with slower decline in mobility and balance. Higher vergence was associated with slower cognitive decline (executive function: Trail Making Test-Part B; visuoperceptual speed: Digit Symbol Substitution Test). Higher optokinetic nystagmus was associated with lower odds of falls and slower decline in balance.ConclusionsSelect eye movement features may be indicators of age-related cognitive and mobility decline. Future studies are warranted to investigate underlying neuroimaging markers and brain pathology.

The role of compensatory eye and head movements for gaze stabilization in the unrestrained frog

Besides allowing us to perceive our surroundings, eye movements are also a window into our mind and a rich source of information on who we are, how we feel, and what we do. Here we show that eye movements during an everyday task predict aspects of our personality. We tracked eye movements of 42 participants while they ran an errand on a university campus and subsequently assessed their personality traits using well-established questionnaires. Using a state-of-the-art machine learning method and a rich set of features encoding different eye movement characteristics, we were able to reliably predict four of the Big Five personality traits (neuroticism, extraversion, agreeableness, conscientiousness) as well as perceptual curiosity only from eye movements. Further analysis revealed new relations between previously neglected eye movement characteristics and personality. Our findings demonstrate a considerable influence of personality on everyday eye movement control, thereby complementing earlier studies in laboratory settings. Improving automatic recognition and interpretation of human social signals is an important endeavor, enabling innovative design of human–computer systems capable of sensing spontaneous natural user behavior to facilitate efficient interaction and personalization.

Reading Chinese, a kind of ideogram, relies more on visual cognition. The visuospatial cognitive deficit of Chinese dyslexia is an interesting topic that has received much attention. The purpose of current research was to explore the visuopatial cognitive characteristics of Chinese dyslexic children by studying their eye movements via a picture searching test. According to the diagnostic criteria defined by ICD-10, twenty-eight dyslexic children (mean age (10.12 +/- 1.42) years) were enrolled from the Clinic of Children Behavioral Disorder in the third affiliated hospital of Sun Yat-sen University. And 28 normally reading children (mean age (10.06 +/- 1.29) years), 1:1 matched by age, sex, grade and family condition were chosen from an elementary school in Guangzhou as a control group. Four groups of pictures (cock, accident, canyon, meditate) from Picture Vocabulary Test were chosen as eye movement experiment targets. All the subjects carried out the picture searching task and their eye movement data were recorded by an Eyelink II High-Speed Eye Tracker. The duration time, average fixation duration, average saccade amplitude, fixation counts and saccade counts were compared between the two groups of children. The dyslexic children had longer total fixation duration and average fixation duration (F = 7.711, P < 0.01; F = 4.520, P < 0.05), more fixation counts and saccade counts (F = 7.498, P < 0.01; F = 11.040, P < 0.01), and a smaller average saccade amplitude (F = 29.743, P < 0.01) compared with controls. But their performance in the picture vocabulary test was the same as those of the control group. The eye movement indexes were affected by the difficulty of the pictures and words, all eye movement indexes, except saccade amplitude, had a significant difference within groups (P < 0.05). Chinese dyslexic children have abnormal eye movements in picture searching, applying slow fixations, more fixations and small and frequent saccades. Their abnormal eye movement mode reflects the poor ability and strategy of visual information processing.

The onset of fatigue is dangerous in areas of activity that require high concentration of human attention, such as air traffic controllers, nuclear power plant operators, etc. It should be noted that these types of activities are characterized by the fact that most of the time the employee sits at the workplace and his gaze is directed at the monitor. The paper presents a method for dynamic detection of PC operator fatigue based on eye movement characteristics. The method for dynamic detection of fatigue implements a training scenario for a fatigue detection model and a fatigue detection scenario. Within the training scenario, eye movement characteristics are calculated and correlations with fatigue test results are searched for. Within the fatigue detection scenario, eye movement characteristics that most strongly correlate with fatigue are selected. These characteristics can also be divided by the types of physical events on which they are based. We can distinguish such characteristics as speed, time, quantity, size, percentage, frequency and ratio characteristics. To find correlations between eye movement characteristics and fatigue, a dataset of eye movements and the results of tests and questionnaires such as the go/no-go task, the Landolt ring test, and the VAS-F questionnaire were analyzed. The dataset consists of gaze coordinate recordings from 15 participants acting as PC operators. To determine the degree of fatigue, the participant completed the VAS-F questionnaire. The Landolt ring correction test is a test used to measure attention concentration. The labeled dataset is used to train a machine learning model that detects fatigue. The experimental results showed that using the characteristics selected in the study yielded the most promising results. This approach allowed us to achieve the highest F-measure and the best average accuracy, indicating the overall reliability of the model.

In this paper, an attention-based network called modified Dense Co-Attention Symmetric Network is investigated to classify three human emotions using electroencephalogram (EEG), eye movement features (EYE), and raw eye movement image (EIG) data. The key idea of this model is to use a novel co-attention layer, which augments the weights of the critical feature channel and builds correlations among the different modalities. We stack the co-attention layer to form a hierarchy that strengthens the effect layer-by-layer to more precisely predict the emotion states. In an emotion recognition task with three modalities (EEG, EYE and EIG), we achieve 87.63% accuracy. To deal with the problem that EEG signals are difficult and expensive to collect in practical applications, we achieve 86.28% accuracy with two modalities (EYE and EIG) and propose a new classification paradigm that uses EIG, EYE, and randomly selected EEG data that we have on hand, obtaining 86.81% accuracy.

The characteristics of children’s eye movements during reading change as they graduallybecome better readers. However, few eye tracking studies have investigated children’sreading and reading development and little is known about the relationship between reading-related eye movement measures and reading assessment outcomes. We recorded andanalyzed three basic eye movement measures in an ecologically valid eye-tracking set-up.The participants were Swedish children (n = 2876) who were recorded in their normalschool environment. The relationship between eye movements and reading assessmentoutcomes was analyzed in using linear mixed effects models. We found similar age-relatedchanges in eye movement characteristics as established in previous studies, and that eyemovements seem to correlate with reading outcome measures. Additionally, our resultsshow that eye movements predict the results on several tests from a word reading assessment.Hence eye tracking may potentially be a useful tool in assessing reading development.

The purpose of this study was to investigate the possibility for evaluating vigilance levels using a parameter of saccadic eye movement. EEG was measured and analyzed by means of fast Fourier transform, and the spectral power of /spl alpha/ and /spl beta/ wave bands were obtained so as to extract [/spl beta/]/[/spl alpha/+/spl beta/] which was assumed to reflect vigilance levels. Eye movement was measured so as to calculate saccadic peak velocity (SPV) and relative SPV (RSPV) which was normalized by the mean SPV at the corresponding amplitude of saccade. The relationship between [/spl beta/]/[/spl alpha/+/spl beta/] and RSPV was investigated and it revealed that RSPV decreased according to the decline in the vigilance level. This indicates the possibility for evaluating vigilance levels using RSPV.

Eye movement is a new kind of feature for biometrical recognition, it has many advantages compared with other features such as fingerprint, face, and iris. It is not only a sort of static characteristics, but also a combination of brain activity and muscle behavior, which makes it effective to prevent spoofing attack. In addition, eye movements can be incorporated with faces, iris and other features recorded from the face region into multimode systems. In this paper, we do an exploring study on eye movement identification based on the eye movement datasets provided by Komogortsev et al. in 2011 with different classification methods. The time of saccade and fixation are extracted from the eye movement data as the eye movement features. Furthermore, the performance analysis was conducted on different classification methods such as the BP, RBF, ELMAN and SVM in order to provide a reference to the future research in this field.