Search Results (1,721)

As geographic information visualization continues to gain prominence, dynamic symbols are increasingly employed in map-based applications. However, the optimal visual coding for dynamic point symbols—particularly concerning encoding type, animation rate, and modulation area—remains underexplored. This study examines how these factors influence user performance in visual search tasks through two eye-tracking experiments. Experiment 1 investigated the effects of two visual coding factors: encoding types (flashing, pulsation, and lightness modulation) and animation rates (low, medium, and high). Experiment 2 focused on the interaction between encoding types and modulation areas (fill, contour, and entire symbol) under a fixed animation rate condition. The results revealed that search performance deteriorates as the animation rate of the fastest target symbol exceeds 10 fps. Flashing and lightness modulation outperformed pulsation, and modulation areas significantly impacted efficiency and accuracy, with notable interaction effects. Based on the experimental results, three visual coding strategies are recommended for optimal performance in map-based interfaces: contour pulsation, contour flashing, and entire symbol lightness modulation. These findings provide valuable insights for optimizing the design of dynamic point symbols, contributing to improved user engagement and task performance in cartographic and geovisual applications. Full article

This study utilized eye-tracking technology to explore the role of visual word segmentation cues in Tibetan reading, with a particular focus on the effects of dictionary-based and psychological word segmentation on reading and lexical recognition. The experiment employed a 2 × 3 design, comparing six conditions: normal sentences, dictionary word segmentation (spaces), psychological word segmentation (spaces), normal sentences (green), dictionary word segmentation (color alternation), and psychological word segmentation (color alternation). The results revealed that word segmentation with spaces (whether dictionary-based or psychological) significantly improved reading efficiency and lexical recognition, whereas color alternation showed no substantial facilitative effect. Psychological and dictionary word segmentation performed similarly across most metrics, though psychological segmentation slightly outperformed in specific indicators (e.g., sentence reading time and number of fixations), and dictionary word segmentation slightly outperformed in other indicators (e.g., average saccade amplitude and number of regressions). The study further suggests that Tibetan reading may involve cognitive processes at different levels, and the basic units of different levels of cognitive processes may not be consistent. These findings hold significant implications for understanding the cognitive processes involved in Tibetan reading and for optimizing the presentation of Tibetan text. Full article

The present study analyzes individual differences in the facilitative processing of grammatical gender by heritage speakers of Spanish, asking whether these differences correlate with lexical proficiency. Results from an eye-tracking study in the Visual World Paradigm replicate prior findings that, as a group, heritage speakers of Spanish show facilitative processing of gender. Importantly, in a follow-up within-group analysis, we test whether three measures of lexical proficiency—oral picture-naming, verbal fluency, and LexTALE—predict individual performance. We find that lexical proficiency, as measured by LexTALE, predicts overall word recognition; however, we observe no effects of the other measures and no evidence that lexical proficiency modulates the strength of the facilitative effect. Our results highlight the importance of carefully selecting tools for proficiency assessment in experimental studies involving heritage speakers, underscoring that the absence of evidence for an effect of proficiency based on a single measure should not be taken as evidence of absence. Full article

Existing research predominantly focuses on the preservation or renewal models of the physical forms of historic cultural districts, with limited exploration of their roles in stimulating tourists’ cognitive, affective resonance, and behavioral interactions. This study addresses historic cultural districts by evaluating the space quality and its impact on tourist experiences through the “cognition-affect-behavior” framework, integrating GIS, street view semantic segmentation, VR eye-tracking, and web crawling technologies. The findings reveal significant multidimensional differences in how space quality influences tourist experiences: the impact intensities of functional diversity, sky visibility, road network accessibility, green visibility, interface openness, and public facility convenience decrease sequentially, with path coefficients of 0.261, 0.206, 0.205, 0.204, 0.201, and 0.155, respectively. Additionally, space quality exerts an indirect effect on tourist experiences through the mediating roles of cognitive, affective, and behavioral dimensions, with a path coefficient of 0.143. This research provides theoretical support and practical insights for empowering cultural heritage space governance with digital technologies in the context of cultural and tourism integration. Full article

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

Pupillometry is commonly used to evaluate cognitive effort, attention, and facial expression response, offering valuable insights into human performance. The combination of eye tracking and facial expression data under the iMotions platform provides great opportunities for multimodal research. However, there is a lack of standardized pipelines for managing pupillometry data on a multimodal platform. Preprocessing pupil data in multimodal platforms poses challenges like timestamp misalignment, missing data, and inconsistencies across multiple data sources. To address these challenges, the authors introduced a systematic preprocessing pipeline for pupil diameter measurements collected using iMotions 10 (version 10.1.38911.4) during an endoscopy simulation task. The pipeline involves artifact removal, outlier detection using advanced methods such as the Median Absolute Deviation (MAD) and Moving Average (MA) algorithm filtering, interpolation of missing data using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and mean pupil diameter calculation through linear regression, as well as normalization of mean pupil diameter and integration of the pupil diameter dataset with facial expression data. By following these steps, the pipeline enhances data quality, reduces noise, and facilitates the seamless integration of pupillometry other multimodal datasets. In conclusion, this pipeline provides a detailed and organized preprocessing method that improves data reliability while preserving important information for further analysis. Full article

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

As large language models (LLMs) and vision–language models (VLMs) become increasingly used in robotics area, a crucial question arises: to what extent do these models replicate human-like cognitive processes, particularly within socially interactive contexts? Whilst these models demonstrate impressive multimodal reasoning and perception capabilities, their cognitive plausibility remains underexplored. In this study, we address this gap by using human visual attention as a behavioural proxy for cognition in a naturalistic human-robot interaction (HRI) scenario. Eye-tracking data were previously collected from participants engaging in social human-human interactions, providing frame-level gaze fixations as a human attentional ground truth. We then prompted a state-of-the-art VLM (LLaVA) to generate scene descriptions, which were processed by four LLMs (DeepSeek-R1-Distill-Qwen-7B, Qwen1.5-7B-Chat, LLaMA-3.1-8b-instruct, and Gemma-7b-it) to infer saliency points. Critically, we evaluated each model in both stateless and memory-augmented (short-term memory, STM) modes to assess the influence of temporal context on saliency prediction. Our results presented that whilst stateless LLaVA most closely replicates human gaze patterns, STM confers measurable benefits only for DeepSeek, whose lexical anchoring mirrors human rehearsal mechanisms. Other models exhibited degraded performance with memory due to prompt interference or limited contextual integration. This work introduces a novel, empirically grounded framework for assessing cognitive plausibility in generative models and underscores the role of short-term memory in shaping human-like visual attention in robotic systems. Full article

Attentional biases seem to play an important role in anorexia nervosa (AN). The objective of this study was to measure visual attention patterns toward female bodies in adolescents and adults with and without AN in order to explore developmental and disease-specific aspects. Female adult and adolescent patients with AN (n = 38) and control participants (n = 39) viewed standardized photographic stimuli showing women’s bodies from five BMI categories. The fixation times on the bodies and specific body parts were analyzed. Differences between participants with and without AN did not emerge: All participants showed increased attention toward the body, while adolescents displayed shorter fixation times on specific areas of the body than adults. Increased visual attention toward areas indicative of weight (e.g., hips, thighs, abdomen, buttocks) and a shorter fixation time on unclothed body parts were observed in all participants. There is evidence for the developmental effect of differential viewing patterns when looking at women’s bodies. The attention behavior of patients with AN seems to be similar to that of the control groups, which is partly consistent with, and partly contradictory to, previous studies. Full article

This study explores the application of Eye-Tracking technology for the ergonomic evaluation of assistive device usability. Sixty-four participants evaluated six jar-opening devices in a two-phase study. First, the participants’ gaze was recorded while they viewed six rendered pictures of assistive devices, each shown in two different versions: with and without rubber in the grip area. Second, the participants physically interacted with the devices in a hands-on usability task. In both phases, participants rated the devices according to six usability affordances: robustness, comfort, easiness to grip, lid slippery, effort level, and easiness to use. Eye-Tracking metrics (fixation duration, number of fixations, and visit duration) correlated with the on-screen ratings, which aligned with ratings after using the physical devices. High ratings in comfort and effort level correlated with more visual attention to the grip area, where the rubber acted as key signifier. Heatmaps revealed the grip area as important for comfort and easiness to use and the lid area for robustness and slipperiness. These findings demonstrate the potential of Eye Tracking in usability studies, providing valuable insights for the ergonomic evaluation of assistive devices. Moreover, they highlight the suitability of Eye Tracking for early-stage design evaluation, offering objective metrics to guide design decisions and improve user experience. Full article

Infants discriminate faces early in the first year, but research on infants’ discrimination of bodies is plagued by mixed findings. Using a familiarisation novelty preference method, we investigated 7- and 9-month-old infants’ discrimination of body postures presented in upright and inverted orientations, and with and without heads, along with relationships with gross and fine motor development. In our initial studies, 7-month-old infants discriminated upright headless postures with forward-facing and about-facing images. Eye tracking revealed that infants looked at the bodies of the upright headless postures the longest and at the heads of upright whole figures for 60–70% of the time regardless of the presence of faces, suggesting that heads detract attention from bodies. In a more stringent test, with similarly complex limb positions between test items, infants could not discriminate postures. With longer trials, the 7-month-olds demonstrated a familiarity preference for the upright whole figures, and the 9-month-olds demonstrated a novelty preference, albeit with a less robust effect. Unlike previous studies, we found that better gross motor skills were related to the 7-month-olds’ better discrimination of upright headless postures compared to inverted postures. The 9-month-old infants’ lower gross and fine motor skills were associated with a stronger preference for inverted compared to upright whole figures. This is further evidence of a configural representation of bodies in infancy, but it is constrained by an upper bias (heads in upright figures, feet in inverted), the test item similarity, and the trial duration. The measure and type of motor development reveals differential relationships with infants’ representations of bodies. Full article

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation. Full article

Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 × 2 × 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific–stratified norms. Full article

Interpreting diagnostic imaging and identifying clinically relevant features remain challenging tasks, particularly for novice radiologists who often lack structured guidance and expert feedback. To bridge this gap, we propose an Eye-Gaze Guided Multimodal Fusion framework that leverages expert eye-tracking data to enhance learning and decision-making in medical image interpretation. By integrating chest X-ray (CXR) images with expert fixation maps, our approach captures radiologists’ visual attention patterns and highlights regions of interest (ROIs) critical for accurate diagnosis. The fusion model utilizes a shared backbone architecture to jointly process image and gaze modalities, thereby minimizing the impact of noise in fixation data. We validate the system’s interpretability using Gradient-weighted Class Activation Mapping (Grad-CAM) and assess both classification performance and explanation alignment with expert annotations. Comprehensive evaluations, including robustness under gaze noise and expert clinical review, demonstrate the framework’s effectiveness in improving model reliability and interpretability. This work offers a promising pathway toward intelligent, human-centered AI systems that support both diagnostic accuracy and medical training. Full article

Lou Gehrig’s disease, also known as ALS, is a progressive neurodegenerative condition that weakens muscles and can lead to paralysis as it progresses. For patients with severe paralysis, eye-tracking devices such as eye mouse enable communication. However, the equipment is expensive, and the calibration process is very difficult and frustrating for patients to use. To alleviate this problem, we propose a simple and efficient method to type texts intuitively with graphical guidance on the screen. Specifically, the method detects patients’ eye blinks in video frames to navigate through three sequential steps, narrowing down the choices from 9 letters, to 3 letters, and finally to a single letter (from a 26-letter alphabet). In this way, a patient is able to rapidly type a letter of the alphabet by blinking a minimum of three times and a maximum of nine times. The proposed method integrates an API of large language model (LLM) to further accelerate text input and correct sentences in terms of typographical errors, spacing, and upper/lower case. Experiments on ten participants demonstrate that the proposed method significantly outperforms three state-of-the-art methods in both typing speed and typing accuracy, without requiring any calibration process. Full article