Eye Movement Analysis Research Articles

Novel metaphor processing in dyslexia: a visual world eye-tracking study

ABSTRACT Metaphor comprehension has been investigated in neurodevelopmental disorders, but studies devoted to adults with dyslexia are few and present inconsistent results. The present study sought to investigate how adults with dyslexia process novel metaphors. Individual differences in vocabulary, working memory, and Theory of Mind were also assessed. An online metaphor comprehension task based on the Visual World Paradigm was carried out with eye-tracking. Metaphors and corresponding literal sentences were aurally presented in isolation, and participants were asked to select a picture that best corresponded to the sentence they heard. Our results indicated that participants with dyslexia chose metaphor interpretations at a similar rate as did the control group. However, online processing data indicated generally slower response times, with a particular delay in processing metaphorical utterances. Eye movement analyses provided further insights into the underlying nature of the processing slowdowns, highlighting specific challenges encountered by individuals with dyslexia when interpreting figurative language.

When function words carry content.

Studies on eye movements during reading have primarily focussed on the processing of content words (CWs), such as verbs and nouns. Those few studies that have analysed eye movements on function words (FWs), such as articles and prepositions, have reported that FWs are typically skipped more often and, when fixated, receive fewer and shorter fixations than CWs. However, those studies were often conducted in languages where FWs contain comparatively little information (e.g., the in English). In Brazilian Portuguese (BP), FWs can carry gender and number marking. In the present study, we analysed data from the RASTROS corpus of natural reading in BP and examined the effects of word length, predictability, frequency and word class on eye movements. Very limited differences between FWs and CWs were observed mostly restricted to the skipping rates of short words, such that FWs were skipped more often than CWs. For fixation times, differences were either nonexistent or restricted to atypical FWs, such as low frequency FWs, warranting further research. As such, our results are more compatible with studies showing limited or no differences in processing speed between FWs and CWs when influences of word length, frequency and predictability are taken into account.

Метод предсказания глазодвигательной активности на основе интеллектуального анализа данных мобильного портативного электроэнцефалографа

Eye movement processes, such as fixations and saccades, play a crucial role in human cognitive activity as they are closely associated with functions such as perception, attention, and decision-making. These processes are actively applied in various fields, including human-computer interaction systems and neurophysiological research. Modern eye-tracking methods based on optical systems provide high accuracy but have several significant limitations. In this regard, the use of electroencephalographic (EEG) data for analyzing eye movement activity is becoming a promising direction, as EEG provides insights into the neural processes underlying eye movements. This allows many limitations of optical systems to be overcome, enabling the monitoring of eye movements without direct visual tracking. The goal of this study is to develop a method for predicting eye movement activity based on data collected from the BrainBit mobile EEG device. The study utilizes a long short-term memory (LSTM) neural network. Experimental results demonstrated that the proposed model achieves high accuracy in classifying the main types of eye movement activity. For fixations and saccades, the classification accuracy reached 91?%, indicating a high effectiveness of the model for these types of movements. However, classifying directed eye movements, such as left/right and up/down movements, proved to be a more challenging task, with an accuracy of about 65?% and 63?%, respectively. One of the key challenges identified in the study was the high individual variability of EEG signals between participants. As a result, the model training was personalized for each participant, which improved the accuracy of predictions for each individual. Thus, the study highlights the potential of using EEG for analyzing and predicting eye movements, particularly in the context of fixations and saccades.

Age- and Sex-Based Developmental Biomarkers in Eye Movements.

Eye movement research serves as a critical tool for assessing brain function, diagnosing neurological and psychiatric disorders, and understanding cognition and behavior. Sex differences have largely been under reported or ignored in neurological research. However, eye movement features provide biomarkers that are useful for disease classification with superior accuracy and robustness compared to previous classifiers for neurological diseases. Neurological diseases have a sex specificity, yet eye movement analysis has not been specific to our understanding of sex differences. The study involved subjects recruited from 804 sites equipped with RightEye Vision Systems, primarily located in optometry practices across the United States. Subjects completed six eye movement assessments: circular smooth pursuit (CSP), horizontal smooth pursuit (HSP), vertical smooth pursuit (VSP), horizontal saccades (HS), vertical saccades (VS), and fixation stability (FS). Eye movements were analyzed and classified in accordance with age and sex by multiple t-tests and linear regression models. This study represented a large sample size of 23,557 subjects, with 11,871 males and 11,686 females representing ages from birth through 80 years of age. We observed statistically significant differences for all eye movement functions between males and females. We demonstrate that eye movements are sex-specific and offer normative data to compare sex-specific eye movement function by age. Novel baseline metrics can be compared to individual performance, regardless of sex. This study represents significant progress in linking eye movements with brain function and clinical syndromes, allowing researchers and clinicians to stratify individuals by age and sex.

Non-invasive eye tracking and retinal view reconstruction in free swimming schooling fish

Eye tracking has emerged as a key method for understanding how animals process visual information, identifying crucial elements of perception and attention. Traditional fish eye tracking often alters animal behavior due to invasive techniques, while non-invasive methods are limited to either 2D tracking or restricting animals after training. Our study introduces a non-invasive technique for tracking and reconstructing the retinal view of free-swimming fish in a large 3D arena without behavioral training. Using 3D fish bodymeshes reconstructed by DeepShapeKit, our method integrates multiple camera angles, deep learning for 3D fish posture reconstruction, perspective transformation, and eye tracking. We evaluated our approach using data from two fish swimming in a flow tank, captured from two perpendicular viewpoints, and validated its accuracy using human-labeled and synthesized ground truth data. Our analysis of eye movements and retinal view reconstruction within leader-follower schooling behavior reveals that fish exhibit negatively synchronised eye movements and focus on neighbors centered in the retinal view. These findings are consistent with previous studies on schooling fish, providing a further, indirect, validation of our method. Our approach offers new insights into animal attention in naturalistic settings and potentially has broader implications for studying collective behavior and advancing swarm robotics.

The eye movement and gait variability analysis in Chinese patients with Huntington's disease.

Huntington's disease disease (HD) is characterized by motor, cognitive, and neuropsychiatric symptoms. Oculomotor impairments and gait variability have been independently considered as potential markers in HD. But there lacks an integration analysis of eye movement and gait. We assessed multiple examinations of eye movement and gait variability in HTT mutation carriers, analyzed the consistency between these parameters and clinical severity, then examined the association between oculomotor impairments and gait deficits. Seven pre-HD, 30 HD patients and 30 age-matched controls were included. We collected demographics and assessed the Unified Huntington's Disease Rating Scale (UHDRS). Examinations including saccade, smooth pursuit and optokinetic (OPK) test were performed to evaluate eye movement function. The parameters of gait include stride length, walking velocity, step deviation, step length and gait phase. There are significant impairments in HD patients in the latency and velocity of saccades, the gain of smooth pursuit as well as the gain and slow phase velocities (SPV) of OPK tests. Only the speed of saccades has significant difference between pre-HD and controls. There are significant impairments of stride length, walking velocity, step length and gait phase in HD patients. Parameters of eye movement and gait variability in HD patients showed consistency with the scores of UHDRS. There were significant correlations between eye movement and gait parameters. Our results show that eye movement and gait are impaired in HD patients, and speed of saccades is early impaired in pre-HD. Eye movement and gait abnormalities in HD are significantly correlated with clinical disease severities.

Inducing strategies to solve a mental rotation task is possible: evidence from a sex-related eye-tracking analysis

The study of spatial skills is gaining importance due to their relevance in everyday activities and their critical role in developing competencies across various academic disciplines. The main goal of this study was to explore whether mental rotation strategies, such as the so-called holistic –rotating an entire object- and piecemeal –rotating individual parts of the object- approaches, can be induced, and whether sex differences emerge during the process of strategy induction. This objective holds a pivotal role as it could lead to the enhancement of mental rotation abilities and the development of effective interventions. To achieve this, a mental rotation task was conducted while eye movements were recorded. In the first block, participants solved the task freely, while in the second block, they received instructions to solve it through either a holistic (42 participants) or a piecemeal (43 participants) strategy in a between-subjects design. In both strategies, participants showed better performance in the second block compared to the first. Males outperformed females. The holistic strategy resulted in faster reaction times in the second block. The number of fixations and saccadic movements decreased in the second block compared to the first for the holistic strategy, while the piecemeal strategy exhibited the opposite ocular pattern. These results indicate that effective mental rotation strategies were successfully elicited. No sex differences were found in the analyzed eye movement variables.

The interference effect of low-relevant animated elements on digital picturebook comprehension in preschoolers: An eye-movement study.

Digital picture-book (DPB) with animated elements can enhance children's engagement, but irrelevant animations may interfere with their comprehension. To determine the effect of the relevance of animated elements on preschoolers' comprehension, an experimental study was conducted. Thirtythree preschoolers between the aged 4-5 years engaged with DPB in three conditions: high- and lowrelevant animations and a static control while listening to the story; their eye movements were recorded simultaneously. The study found that preschoolers had lower comprehension when exposed to low-relevant animation, but had comparable scores to the static condition with high-relevant animation. The results of eye-movement analysis showed that children who focused less on highrelevant or more on low-relevant elements had poorer comprehension. Those exposed to low-relevant animations looked less at high-relevant elements and more at low-relevant elements than those in the static and high-relevant conditions. These results suggested that low-relevant animations in DPB interfered with children's comprehension by directing their visual attention away from crucial, highrelevant elements and more to less relevant elements. Therefore, designers creating DPBs, as well as parents and caregivers selecting DPBs for children, should consider the importance of the relevance of animated elements. And the corresponding mechanism of animation effect in DPB comprehension was discussed.

Analysis of eye movement features in patients with Alzheimer's disease based on intelligent eye movement analysis and evaluation system.

The early identification of Alzheimer's disease (AD) benefits patients, so creating a simple and convenient method is crucial for diagnosing early symptoms. To offer a potential approach for the early detection of both AD and mild cognitive impairment (MCI). Eye movement data from 66 patients were divided into three groups, including healthy control group (HC), MCI group, and AD group. We searched for parameters that can detect MCI at an early stage and drew receiver operating characteristic (ROC) curves. The correlation between eye movement parameters and cognitive scores was analyzed. The MCI group differed from the HC group in error correction rate of antisaccade (p = 0.008) and total offset degrees (>4°) (p = 0.011) of lateral fixation. The AD group had different overlap prosaccade accuracy (p = 0.025), latency (p = 0.009) and average completion time (p = 0.015), gap prosaccade latency (p = 0.005) and average completion time (p = 0.005), antisaccade accuracy (p = 0.006), error correction rate (p < 0.001) and average saccade velocity (p = 0.035), and lateral fixation accuracy (p = 0.018), total offset degrees (>4°) (p = 0.041) compared to the HC group. The AD group differed significantly from the MCI group in accuracy (p = 0.001) and error correction rate (p = 0.044) of antisaccades, the latency (p = 0.009) and average completion time (p = 0.025) of overlap prosaccade and the latency (p = 0.038) of gap prosaccade, these parameters can serve as indicators to monitor the progress of the disease. Lateral fixation combined with antisaccade was more conducive to identifying MCI patients with the area under the ROC curve of 0.837. Most eye movement parameters had a light to moderate correlation with cognitive scores. Eye movements can be used for early identification of MCI/AD patients and to monitor disease progression.

The effects of a dual task on gaze behavior examined during a simulated flight in low-time pilots.

Cognitive load can impair an operator's ability to optimally scan and process relevant information that is critical to the safe and successful operation of an aircraft. Since the cognitive demands experienced by pilots fluctuate throughout a given flight due to changes in task demands that range from high to low cognitive load, it has become increasingly important to objectively track and quantify these changes accordingly. The analysis of eye movements has been shown to be a promising method to understand information acquisition, processing efficiency, and how these aspects of cognition impact pilot performance. Therefore, the aim of the current study was to assess the impact of a dual task paradigm on low-time pilot flight performance and gaze behavior during two phases of flight with varying levels of cognitive load. Twenty-two licensed pilots (<350 h) completed simulated flight circuits alongside an auditory oddball task under visual flight rules conditions. Self-reported situation awareness scores and auditory task performance revealed the dual task was more demanding than the single tasks. Flight performance and gaze behavior indicated that primary task performance and information processing remained unaffected. These results suggest that the recruited pilots attained a level of skill proficiency that enabled the efficient deployment of cognitive resources to successfully complete the flying task under states of increased cognitive load. Combined with previous research findings, the results suggest that the effect of secondary tasks depends on the type of tasks used (i.e., simple/choice response tasks, memory recall, etc.). The utility of using a dual task and gaze behavior to probe flight proficiency and information processing efficiency throughout training are discussed.

Imaging, biomarkers, and vascular cognitive impairment in China: Rationale and design for the VICA study.

Vascular cognitive impairment (VCI) is highly heterogeneous, with unclear pathogenesis. Individuals with vascular risk factors (VRF), cerebral small vessel disease (CSVD), and stroke are all at risk of developing VCI. To address the growing challenges posed by VCI, the "Vascular, Imaging and Cognition Association of China" (VICA) was established. VICA aims to recruit 10,000 participants, including 2000 with VRF, 3000 with CSVD, and 5000 stroke patients, to form a nationwide multicenter cohort. The study integrates clinical, neuroimaging, and multi-omics data to better understand VCI heterogeneity, improve disease prediction, and ensure timely diagnosis. VICA has screened 2045 eligible VRF participants from six communities in Wuhan, Shanghai, and Taizhou, along with 602 CSVD and 1269 stroke patients from 135 hospitals nationwide. Baseline enrollment and follow-up work are still ongoing. Establishing a high-quality longitudinal cohort is crucial for understanding VCI pathogenesis and developing novel markers for early screening and diagnosis. Establish a large-scale prospective longitudinal cohort comprising 10,000 participants, focusing on the high-risk population of vascular cognitive impairment (VCI) in China. Establish a nationwide three-tier medical network, make full use of resources, and achieve extensive enrollment of patients with cerebral small vessel disease and stroke patients. Utilize multimodal imaging and biomarkers to lay the foundation for constructing more-precise risk models. Introduce eye movement and gait analysis as new methods for assessing cognitive function. Use positron emission tomography to further investigate the interaction between vascular factors and neurodegeneration.

Advancement of Discrimination Accuracy of Musical Development Degree Utilizing Machine Learning with Optimized Feature Quantities Extracted from Simultaneous Analysis of Both Eye Movement and Body Movement During Musical Expression in Early Childhood

To measure musical development of children by expert observation has been essential part of musical education. However, how exactly evaluate such development degree is often hard to quantify. A valid and reliable tool is needed, ideally measuring objective movement data extracted from full body including eyes. This study aims to extract feature quantities effectively contributing to classification of developmental process of musical expression in early childhood through the simultaneous analysis of both eye movement and body movement in 3-year-old, 4-year-old, and 5-year-old’s musical expression for the specific song. Therefore, the calculated data for the two years of 2022 and 2023 were applied to a two-way analysis of variance by the facility factor and age factor. As a result, a statistically significant difference was observed in the moving distance and the moving average velocity such as head, right shoulder, and right hand, and the moving average acceleration such as right shoulder and right hand, and the moving smoothness of right hand. Furthermore, feature quantities were extracted from those results of quantitative analysis based on simultaneous analysis of both eye movement and body movement in musical expression from 2022 year to 2023 year, implemented to machine learning and the classification accuracy was calculated. Vision is obviously important to facilitate movement and stabilization of posture as well as to enable music induced body parts motion. As a result of machine learning, several classifiers such as NN (neural network model) and SVM (support vector machine) showed a better classification accuracy when implementing the both eye movement data and body movement data in musical expression in the simultaneous analysis than only body movement data in musical expression. Specifically, a result when both eye movement data and body movement data in musical expression applying to machine learning using NN showed 74.42 % in classification accuracy although 55.81% in the classification accuracy by only body movement data. Those results verified greater efficacy of simultaneous analysis in musical expression and strong linkage between eye movement and body movement in musical expression in early childhood.

The impact of point symbol similarity on visual search on maps

ABSTRACT The study presented in this paper investigates the impact of the similarity level of cartographic point symbols on visual search performance. Specifically, we explore the application of an algorithm for quantitatively assessing symbols’ shape similarity, and apply the results of such automated analysis in a simple target search task on a map with distractors of varying similarity levels. Our primary hypothesis posits that the more similar the target symbol and distractors, the more challenging the visual search becomes in terms of search efficiency and eye metric performance. In a controlled laboratory experiment utilizing eye-tracking device, 33 participants were recruited, and 60 map stimuli were tested with each participant. The results confirmed the hypothesis assumptions. However, symbols based on a round shape were consistently the easiest to find at every similarity level. Eye movement analysis revealed differences in absolute pupil size between male and female participants, possibly associated with cognitive load, although without affecting search time performance. Interestingly, scan-path speed decreased with an increasing level of similarity between the target symbol and distractors. The practical implication of this study lies in the potential development of tools to assist map designers in selecting symbols with low similarity, thereby ensuring improved map readability.

Towards Automatic Object Detection and Activity Recognition in Indoor Climbing

Rock climbing has propelled from niche sport to mainstream free-time activity and Olympic sport. Moreover, climbing can be studied as an example of a high-stakes perception-action task. However, understanding what constitutes an expert climber is not simple or straightforward. As a dynamic and high-risk activity, climbing requires a precise interplay between cognition, perception, and precise action execution. While prior research has predominantly focused on the movement aspect of climbing (i.e., skeletal posture and individual limb movements), recent studies have also examined the climber’s visual attention and its links to their performance. To associate the climber’s attention with their actions, however, has traditionally required frame-by-frame manual coding of the recorded eye-tracking videos. To overcome this challenge and automatically contextualize the analysis of eye movements in indoor climbing, we present deep learning-driven (YOLOv5) hold detection that facilitates automatic grasp recognition. To demonstrate the framework, we examined the expert climber’s eye movements and egocentric perspective acquired from eye-tracking glasses (SMI and Tobii Glasses 2). Using the framework, we observed that the expert climber’s grasping duration was positively correlated with total fixation duration (r = 0.807) and fixation count (r = 0.864); however, it was negatively correlated with the fixation rate (r = −0.402) and saccade rate (r = −0.344). The findings indicate the moments of cognitive processing and visual search that occurred during decision making and route prospecting. Our work contributes to research on eye–body performance and coordination in high-stakes contexts, and informs the sport science and expands the applications, e.g., in training optimization, injury prevention, and coaching.

Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation

Real-world work environments require operators to perform multiple tasks with continual support from an automated system. Eye movement is often used as a surrogate measure of operator attention, yet conventional summary measures such as percent dwell time do not capture dynamic transitions of attention in complex visual workspace. This study analyzed eye movement data collected in a controlled a MATB-II task environment using gaze transition entropy analysis. In the study, human subjects performed a compensatory tracking task, a system monitoring task, and a communication task concurrently. The results indicate that both gaze transition entropy and stationary gaze entropy, measures of randomness in eye movements, decrease when the compensatory tracking task required more continuous monitoring. The findings imply that gaze transition entropy reflects attention allocation of operators performing dynamic operational tasks consistently.

Eye movements in conceptual and non-conceptual thinking

AbstractHow to influence and assess whether students engage in conceptual thinking are longstanding methodological problems in mathematics education. Recently, eye-tracking technology has fueled a discussion on whether eye movement analysis can support valid inferences about mathematical thinking. This study investigates whether eye movement analysis can distinguish between conceptual and non-conceptual thinking in a geometric classification task where both modes of thinking lead to identical responses. Participants were asked to classify geometric shapes while we tracked their eye movements and to report their thinking verbally. Our findings indicate that self-reported conceptual thinking is characterised by fewer eye movements between task shapes and response shapes, and that self-reported non-conceptual thinking involves comparing the shapes’ similarity directly. A logistic regression model correctly classified the self-reported ways of thinking in 80.3% of the cases. We conclude that eye-tracking can contribute to making inferences about mathematical thought processes and facilitate research on how to engage students in conceptual thinking and development.

Eyes as a window to brain pathology in parkinson's disease: a narrative review.

There is a renewed interest on eye movements analysis and retinal alterations in Parkinson's disease. This may identify markers for at-risk subpopulation, early diagnosis and evolutive profiles for research or personalized medicine.

An integrated framework for eye tracking-assisted task capability recognition of air traffic controllers with machine learning

To effectively address the continuously increasing demands of air transport, air traffic management (ATM) systems are evolving towards a human-artificial intelligence (AI) hybrid automation paradigm. In this paradigm, air traffic controllers (ATCOs) play a crucial role in ensuring safe and efficient operations. Recognizing ATCOs’ task capability is essential for evaluating performance, optimizing task assignments, and personalizing training strategies. Physiological signals, such as eye movements, offer objective insights into human behavior and cognitive processes, making them valuable for identifying the task capability of ATCOs. In this study, an integrated framework leveraging machine learning is proposed to achieve this goal. First, a Transformer-attentional long short-term memory (LSTM) network is developed to analyze eye movement patterns, capturing both global and long-term dependencies for precise skill level detection. Next, manual parameters are extracted from the raw eye tracking data and the SHAP (SHapley Additive exPlanation) method is utilized to determine the importance of these parameters, aiding in the selection of relevant performance metrics. Furthermore, a radar chart is implemented to intuitively visualize and compare performance metrics across different skill levels based on the selected parameters. A case study and extensive experiments are conducted to validate the effectiveness of the proposed framework. This research advances task capability recognition for ATCOs in a human-in-the-loop scenario, with a focus on expertise level detection, parameter importance identification, and performance metric comparison.

Online Metaphor Comprehension in Adults with Autism Spectrum Disorders: An Eye Tracking Study

AbstractThe aim of this study was to investigate novel metaphor comprehension in adults with autism spectrum disorder (ASD). Previous literature is conflicting about whether individuals with ASD have impairment in this particular type of figurative language. Participants in the study completed a visual world paradigm eye-tracking task, which involved selecting an interpretation of an auditorily presented sentence (i.e. a picture-sentence matching task), where images corresponded to literal and metaphorical interpretations. Thus, the study also investigated online processing, via reaction times and eye movements. Forty adults participated in the study (18 with ASD and 22 typically-developing controls). Each participant completed the AQ questionnaire and had their vocabulary assessed. Results showed that participants with ASD comprehended metaphorical utterances with the same accuracy as controls. However, they had significantly slower reaction times, and specifically, were approximately 800 ms slower. Analysis of eye movements revealed that participants with ASD showed significantly longer fixation times on both the target and distractor image, the latter of which suggests difficulty overcoming the literal interpretation. Consistent with some prior studies, we showed that adults with ASD are not impaired in novel metaphor comprehension, but they were clearly less efficient. Verbal abilities did not significantly relate to performance. Finally, our online processing measure (eye tracking) provided us with insights into the nature of the ASD inefficiency (i.e. a literality bias).

Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture

The analysis of eye movements has proven valuable for understanding brain function and the neuropathology of various disorders. This research aims to utilize eye movement data analysis as a screening tool for differentiation between eight different groups of pathologies, including scholar, neurologic, and postural disorders. Leveraging a dataset from 20 clinical centers, all employing AIDEAL and REMOBI eye movement technologies this study extends prior research by considering a multi-annotation setting, incorporating information from recordings from saccade and vergence eye movement tests, and using contextual information (e.g. target signals and latency of the eye movement relative to the target and confidence level of the quality of eye movement recording) to improve accuracy while reducing noise interference. Additionally, we introduce a novel hybrid architecture that combines the weight-sharing feature of convolution layers with the long-range capabilities of the transformer architecture to improve model efficiency and reduce the computation cost by a factor of 3.36, while still being competitive in terms of macro F1 score. Evaluated on two diverse datasets, our method demonstrates promising results, the most powerful discrimination being Attention & Neurologic; with a macro F1 score of up to 78.8%; disorder. The results indicate the effectiveness of our approach in classifying eye movement data from different pathologies and different clinical centers accurately, thus enabling the creation of an assistant tool in the future.