Eye Movements During Everyday Behavior Predict Personality Traits

Previous studies utilizing machine learning methods have demonstrated that personal traits can be predicted from eye movement data recorded in real-world situations, such as navigating a university campus or browsing one's Facebook news feed. The objective of this study was to conceptually replicate and extend these findings in a different type of visual engagement. Specifically, we aimed to predict individuals' personal traits using eye movements that reflect gaze behaviors while reading manga (Japanese comics). We recorded the eye movements of 51 participants as they read manga and trained several machine learning classifiers to predict the levels of each of the self-reported Big Five personality traits from the eye movement features extracted from their reading behavior. The models' performance was evaluated using cross-validation, and the SHapley Additive exPlanation (SHAP) approach was employed to elucidate the classification model by identifying important features and their impacts on the model output. Among the Big Five personality traits, only extraversion was predictable. The evaluation results demonstrated that the best model achieved comparable performance with previous literature, with a macro F1 score of 0.49. Analysis of the SHAP value plots showed that a high fixation rate, pupillary response, and blink rate were informative indicators. The results partially replicated the previously noted associations between eye movement and personality traits. We found that gaze behaviors observed during reading manga are informative of an individual's extraversion personality trait. We also point out several potential advantages of using manga for gaze-based personality detection.

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.

This paper aims to examine the relationship between personality traits (extraversion, neuroticism, agreeableness, openness, and conscientiousness) and cigarette smoking behavior and also determine the moderating effect of self-efficacy on the relationship between personality traits and cigarette smoking behavior among adolescents in Kerman, Iran. A quantitative research method was employed. The samples included three hundred current smoker adolescents between the ages of 15 to 18. The data was collected by means of self-administered questionnaires and then analyzed by AMOS software. The direct structural model was employed to figure out the path relationships between personality traits (extraversion, neuroticism, agreeableness, openness, and conscientiousness) and cigarette smoking behavior. In addition, a multi-group analysis approach was applied to assess the moderating effect of self-efficacy on the relationship between personality traits (extraversion, neuroticism, agreeableness, openness, and conscientiousness) and cigarette smoking behavior among adolescents. The results of testing the structural equation model showed that there were significant relationships between personality traits (neuroticism and conscientiousness) and cigarette smoking behavior whereas there was no significant relationship between personality traits (extraversion, agreeableness and openness) and cigarette smoking behavior. Also, the results related to the moderating effect of self-efficacy revealed that self-efficacy significantly moderated the effect of neuroticism on cigarette smoking behavior but did not moderate the relationship of other personality traits i.e. extraversion, openness, agreeableness and conscientiousness with and cigarette smoking behavior. At the end, this study recommends some solutions in order to prevent cigarette smoking behavior among adolescents.

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.

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.

Oculomotor behavior and perceptual strategies in complex tasks

The COVID-19 pandemic has affected the lives of people worldwide. Governments struggled to persuade citizens to obey ongoing lockdowns and social restrictions to fight the transmission of the virus. The purpose of this paper is to investigate the impact of prosocial behavior during COVID-19 and big-five personality traits on compliance with health-protective behavior against COVID-19. To examine possible predictors an online questionnaire was delivered to undergraduate students at a public university of Athens during the second phase of the pandemic (November 2020). A mediation analysis was performed to test the relationships among variables. The sample consisted of 239 business school students and the results revealed that two of the big-five personality traits of young adults, conscientiousness, and neuroticism, can be linked with a positive attitude to following heath precautions and recommendations while conscientiousness and agreeableness are predictors of young adults’ prosociality behavior to cope with the COVID-19 pandemic. Finally, the results indicated that there is not any effect of young adults’ personality traits on health recommendation through the mediation of prosociality. The results will contribute to the recent literature (Campos-Mercade, Meier, Schneider, & Wengström, 2021; Miles, Andiappan, Upenieks, & Orfanidis, 2021) on the factors influencing prosocial decision-making regarding the pandemic.

Introduction: Physical, emotional, and behavioural changes are hallmarks of stress, which is a normal human reaction to demanding or difficult circumstances. High stress levels can significantly impact the academic performance and mental health of young students. Chronotype represents genetically determined behavioural characteristics of a person’s twentyfour-hour activity cycle and is pivotal in mental health. Individual differences in stress experiences may be partially explained by personality traits. Individuals with greater emotional intelligence tend to exhibit lower stress reactivity. Aim: To assess the influence of chronotype, personality, and Emotional Competence (EC) on perceived stress among undergraduate medical students. Materials and Methods: This cross-sectional study was conducted in the Department of Physiology at Panimalar Medical College Hospital and Research Institute in Chennai, Tamil Nadu, India, from November 2024 to January 2025. It involved 114 first-year medical students, with the primary inclusion criterion being that participants were first-year medical undergraduates aged 18-25 years. Both genders were included. Participants, after giving informed consent, completed the Horne-Ostberg Morningness-Eveningness Questionnaire (MEQ), the Big Five Inventory, the Profile of EC, and the Perceived Stress Scale via Google Forms. Statistical Package for the Social Sciences (SPSS) version 29 was used for data analysis. Spearman’s correlation test was employed to examine the association between perceived stress and additional variables. Multiple regression analysis was performed to predict the level of perceived stress based on variables such as chronotype, EC, and personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). Statistical significance was set at p<0.05. Results: The mean age of the study population was 18.75±0.64 years, out of which 80 were females and 34 males. The data indicated that most participants had an intermediate chronotype 69 (60.5%), followed by the morning type 31 (27.2%) and evening type 14 (12.3%). A significant, strong positive correlation was observed between neuroticism and perceived stress (r=0.74, p<0.01). A significant negative correlation was found between perceived stress and the measured variables, namely chronotype (r=-0.25, p=0.01), conscientiousness (r=- 0.35, p<0.001), extraversion (r=-0.49, p<0.001), agreeableness (r=-0.27, p<0.001), intrapersonal EC (r=-0.53, p<0.001), interpersonal competence (r=-0.29, p<0.001), and global EC (r=-0.52, p<0.001). Regression analysis revealed extraversion as a negative predictor and neuroticism as a positive predictor of perceived stress among medical students. Conclusion: The present study reveals complex relationships between chronotype, personality traits, EC, and perceived stress among students. Intermediate chronotypes were the most common, with morningness negatively correlated to stress. Personality traits, especially extraversion and neuroticism, significantly predicted stress levels. Higher EC was associated with lower perceived stress, potentially indicating better stress management skills. These findings contribute to the understanding of factors influencing stress perception in students and may inform targeted interventions for stress management in academic settings.

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.

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.

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.

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.

Childhood intelligence and personality traits neuroticism and openness contributes to social mobility: A study in the Aberdeen 1936 Birth Cohort

The present paper aims at describing the concept of how the interaction with computer devices may benefit from joint input of users emotional state and his/her eye movements characteristics. We claim that emotions regulate visual attention, changing its formal characteristics and thus in turn influence the user's behaviour. We hypothesised that during interaction with an interface, positive mood will enhance exploratory eye movements. Participants of the study completed positive or neutral mood induction procedure followed by free viewing of classical paintings on computer screen. During the whole experimental procedure, the users' eye movements as well as their brain activity were recorded. Results confirm that positive mood changes the dynamics of visual attention. When completing a computer task, people's eye movements indicate shift from ambient toward more focal attention mode. However, positive mood slows down that process, fostering visual exploration of the presented stimuli. Obtained results are discussed in the context of self-adaptive user interfaces idea suggesting that the information about the users' emotional state together with visual attention characteristics and gaze information could be used as an input channel in order to create better user experience.