Do your eye movements reveal your performance on an IQ test? A study linking eye movements and socio-demographic information to fluid intelligence

Microsaccade selectivity as discriminative feature for object decoding

Microsaccades, a form of fixational eye movements, help maintain visual stability during stationary observations. This study examines the modulation of microsaccadic rates by various stimulus categories in monkeys and humans during a passive viewing task. Stimulus sets were grouped into four primary categories: human, animal, natural, and man-made. Distinct post-stimulus microsaccade patterns were identified across these categories, enabling successful decoding of the stimulus category with accuracy and recall of up to 85%. We observed that microsaccade rates are independent of pupil size changes. Neural data showed that category classification in the inferior temporal (IT) cortex peaks earlier than changes in microsaccade rates, suggesting feedback from the IT cortex influences eye movements after stimulus discrimination. These results contribute to neurobiological models, enhance human-machine interfaces, optimize experimental visual stimuli, and deepen understanding of microsaccades' role in object decoding.

Prediction of Attention Groups and Big Five Personality Traits from Gaze Features Collected from an Outlier Search Game

This study explores the intersection of personality, attention and task performance in traditional 2D and immersive virtual reality (VR) environments. A visual search task was developed that required participants to find anomalous images embedded in normal background images in 3D space. Experiments were conducted with 30 subjects who performed the task in 2D and VR environments while their eye movements were tracked. Following an exploratory correlation analysis, we applied machine learning techniques to investigate the predictive power of gaze features on human data derived from different data collection methods. Our proposed methodology consists of a pipeline of steps for extracting fixation and saccade features from raw gaze data and training machine learning models to classify the Big Five personality traits and attention-related processing speed/accuracy levels computed from the Group Bourdon test. The models achieved above-chance predictive performance in both 2D and VR settings despite visually complex 3D stimuli. We also explored further relationships between task performance, personality traits and attention characteristics.

Quantifying attention in children with intellectual and developmental disabilities through multicenter electrooculogram signal analysis

In a multicenter case-control investigation, we assessed the efficacy of the Electrooculogram Signal Analysis (EOG-SA) method, which integrates attention-related visual evocation, electrooculography, and nonlinear analysis, for distinguishing between intellectual and developmental disabilities (IDD) and typical development (TD) in children. Analyzing 127 participants (63 IDD, 64 TD), we applied nonlinear dynamics for feature extraction. Results indicated EOG-SA's capability to distinguish IDD, with higher template thresholds and Correlation Dimension values correlating with clinical severity. The template threshold proved a robust indicator, with higher values denoting severe IDD. Discriminative metrics showed areas under the curve of 0.91 (template threshold) and 0.85/0.91 (D2), with sensitivities and specificities of 77.6%/95.9% and 93.5%/71.0%, respectively. EOG-SA emerges as a promising tool, offering interpretable neural biomarkers for early and nuanced diagnosis of IDD.

Taking another look at intelligence and personality using an eye-tracking approach

Intelligence and personality are both key drivers of learning. This study extends prior research on intelligence and personality by adopting a behavioral-process-related eye-tracking approach. We tested 182 adults on fluid intelligence and the Big Five personality traits. Eye-tracking information (gaze patterns) was recorded while participants completed the intelligence test. Machine learning models showed that personality explained 3.18% of the variance in intelligence test scores, with Openness and, surprisingly, Agreeableness most meaningfully contributing to the prediction. Facet-level measures of personality explained a larger amount of variance (7.67%) in intelligence test scores than the trait-level measures, with the largest coefficients obtained for Ideas and Values (Openness) and Compliance and Trust (Agreeableness). Gaze patterns explained a substantial amount of variance in intelligence test performance (35.91%). Gaze patterns were unrelated to the Big Five personality traits, but some of the facets (especially Self-Consciousness from Neuroticism and Assertiveness from Extraversion) were related to gaze. Gaze patterns reflected the test-solving strategies described in the literature (constructive matching, response elimination) to some extent. A combined feature vector consisting of gaze-based predictions and personality traits explained 37.50% of the variance in intelligence test performance, with significant unique contributions from both personality and gaze patterns. A model that included personality facets and gaze explained 38.02% of the variance in intelligence test performance. Although behavioral data thus clearly outperformed "traditional" psychological measures (Big Five personality) in predicting intelligence test performance, our results also underscore the independent contributions of personality and gaze patterns in predicting intelligence test performance.

The impact of presentation modes on mental rotation processing: a comparative analysis of eye movements and performance

Mental rotation is the ability to rotate mental representations of objects in space. Shepard and Metzler's shape-matching tasks, frequently used to test mental rotation, involve presenting pictorial representations of 3D objects. This stimulus material has raised questions regarding the ecological validity of the test for mental rotation with actual visual 3D objects. To systematically investigate differences in mental rotation with pictorial and visual stimuli, we compared data of N = 54 university students from a virtual reality experiment. Comparing both conditions within subjects, we found higher accuracy and faster reaction times for 3D visual figures. We expected eye tracking to reveal differences in participants' stimulus processing and mental rotation strategies induced by the visual differences. We statistically compared fixations (locations), saccades (directions), pupil changes, and head movements. Supplementary Shapley values of a Gradient Boosting Decision Tree algorithm were analyzed, which correctly classified the two conditions using eye and head movements. The results indicated that with visual 3D figures, the encoding of spatial information was less demanding, and participants may have used egocentric transformations and perspective changes. Moreover, participants showed eye movements associated with more holistic processing for visual 3D figures and more piecemeal processing for pictorial 2D figures.

Grandiose narcissism associates with higher cognitive performance under stress through more efficient attention distribution: An eye-tracking study

Narcissism is a part of the Dark Triad that consists also of the traits of Machiavellianism and psychopathy. Two main types of narcissism exist: grandiose and vulnerable narcissism. Being a Dark Triad trait, narcissism is typically associated with negative outcomes. However, recent research suggests that at least the grandiose type may be linked (directly or indirectly) to positive outcomes including lower levels of psychopathology, higher school grades in adolescents, deeper and more strategic learning in university students and higher cognitive performance in experimental settings. The current pre-registered, quasi-experimental study implemented eye-tracking to assess whether grandiose narcissism indirectly predicts cognitive performance through wider distribution of attention on the Raven's Progressive Matrices task. Fifty-four adults completed measures of the Dark Triad, self-esteem and psychopathology. Eight months to one year later, participants completed the Raven's, while their eye-movements were monitored during high stress conditions. When controlling for previous levels of psychopathology, grandiose narcissism predicted higher Raven's scores indirectly, through increased variability in the number of fixations across trials. These findings suggest that grandiose narcissism predicts higher cognitive performance, at least in experimental settings, and call for further research to understand the implications of this seemingly dark trait for performance across various settings.

Evidence in Support of Analogical Reasoning Improvements with Executive Attention Intervention in Healthy Young Adults

Analogical reasoning improvement is important in educational outcome improvement. Inspired by recent ideas and evidence, we applied anti-saccade task training as an executive attention intervention and tested whether it could improve analogical reasoning performance. A serial-task paradigm was applied where participants performed an anti-saccade followed by an analogical reasoning task including a perception condition. The experimental group finished the anti-saccade task in which the ratio of anti-saccade trials to pro-saccade trials was 5:1 while the counterpart was 1:1 in the active control group. Also, a blank control group was established where participants merely finished the analogical reasoning task. Event-related electroencephalographic (EEG) data were recorded when participants were performing the executive attention and analogical reasoning tasks. In addition, their resting state EEG was collected before and after the executive attention intervention. Behaviorally, the experimental group reacted significantly faster than the other two groups in analogical reasoning but not in perception. At the neural level, in the experimental group alone, the anti-saccade trials elicited a smaller N2 than pro-saccade trials and the resting alpha power was improved after executive attention intervention. No significant difference in P2 was found between the two groups in analogical reasoning or perception but the experimental group showed a larger late positive component than the active control group in analogical reasoning. We also found that the late positive component mediated the relationship between the N2 of anti-saccade trials and analogical reasoning reaction times in the experimental group. We further discussed the role of executive attention in the analogical reasoning process, which may pave the way for the future reliable improvement of fluid intelligence.

Exploring Gender Differences in Computational Thinking Learning in a VR Classroom: Developing Machine Learning Models Using Eye-Tracking Data and Explaining the Models

Understanding existing gender differences in the development of computational thinking skills is increasingly important for gaining valuable insights into bridging the gender gap. However, there are few studies to date that have examined gender differences based on the learning process in a realistic classroom context. In this work, we aim to investigate gender classification using students’ eye movements that reflect temporal human behavior during a computational thinking lesson in an immersive VR classroom. We trained several machine learning classifiers and showed that students’ eye movements provide discriminative information for gender classification. In addition, we employed a Shapley additive explanation (SHAP) approach for feature selection and further model interpretation. The classification model trained with the selected (best) eye movement feature set using SHAP achieved improved performance with an average accuracy of over $$70\%$$ 70 % . The SHAP values further explained the classification model by identifying important features and their impacts on the model output, namely gender. Our findings provide insights into the use of eye movements for in-depth investigations of gender differences in learning activities in VR classroom setups that are ecologically valid and may provide clues for providing personalized learning support and tutoring in such educational systems or optimizing system design.