Search Results (244)

This paper presents the design and feasibility evaluation of a low-cost, head-mounted pupil dilation tracking system based on computer vision. The proposed solution employs a standard webcam and active infrared illumination, enabling stable eye image acquisition under controlled lighting conditions. The developed image processing pipeline incorporates adaptive contrast enhancement and geometric pupil detection, allowing for the estimation of relative changes in pupil diameter in real time. System evaluation was conducted in a controlled experiment involving 24 participants performing an N-back task with emotional modulation, a well-established paradigm for eliciting task-evoked pupillary responses under constant working-memory demands. The results revealed statistically significant changes in relative pupil dilation in response to stimuli with varying emotional valence during a working memory task, confirming the system’s ability to capture task-evoked pupillary responses (TEPRs). The proposed system constitutes a low-cost research tool for studies of task engagement and physiological responses in the context of human–computer interaction and psychophysiology, with a focus on the analysis of functional pupilometric changes. Full article

Wound healing has been studied extensively in humans and lab animals, but not in dolphins. Severe human wounds require extensive medical intervention to avoid infection. Yet severe wounds on free-ranging dolphins heal without infection in microbial-infested seas, a compelling distinction. An eye-witnessed shark attack on a yearling bottlenose dolphin yielded 8 years of macroscopic markers on a live recuperating dolphin by known days of healing. In total, 106 healing histories were generated from the author’s 20-year ethological study of free-ranging bottlenose dolphins (Tursiops truncatus) in St. Petersburg, FL, USA. Results show that unaided wound healing at sea involves two consecutive macroscopic pigment patterns, wounds form preliminary seals by 4–8 weeks, and most heal to atrophic scars that remodel for years. Macroscopic markers in live recuperating dolphins show strong matches with macroscopic wound patterns in stranded Fraser’s dolphins (Lagenodelphis hosei), demonstrating links between macroscopic markers and immune activities. This is the first study to link macroscopic markers visible as healing-related pigment patterns to immunity. Macroscopic markers are conservation tools for tracking anthropogenic impacts on increased susceptibility to infection at sea and could lead to novel therapies in veterinary and human regenerative medicine. Full article

Background: Amyotrophic lateral sclerosis (ALS) progressively impairs motor function, compromising speech and limiting communication. Augmentative and alternative communication (AAC) is essential to maintain autonomy, social participation, and quality of life for people with ALS (PALS). This review maps technological developments in AAC, from low-tech tools to advanced brain–computer interface (BCI) systems. Methods: We conducted a scoping review following the PRISMA extension for scoping reviews. PubMed, Web of Science, SciELO, MEDLINE, and CINAHL were screened for studies published up to 31 August 2025. Peer-reviewed RCT, cohort, cross-sectional, and conference papers were included. Single-case studies of invasive BCI technology for ALS were also considered. Methodological quality was evaluated using JBI Critical Appraisal Tools. Results: Thirty-seven studies met inclusion criteria. High-tech AAC—particularly eye-tracking systems and non-invasive BCIs—were most frequently studied. Eye tracking showed high usability but was limited by fatigue, calibration demands, and ocular impairments. EMG- and EOG-based systems demonstrated promising accuracy and resilience to environmental factors, though evidence remains limited. Invasive BCIs showed the highest performance in late-stage ALS and locked-in syndrome, but with small samples and uncertain long-term feasibility. No studies focused exclusively on low-tech AAC interventions. Conclusions: AAC technologies, especially BCIs, EMG and eye-tracking systems, show promise in supporting autonomy in PALS. Implementation gaps persist, including limited attention to caregiver burden, healthcare provider training, and the real-world use of low-tech and hybrid AAC. Further research is needed to ensure that communication solutions are timely, accessible, and effective, and that they are tailored to functional status, daily needs, social participation, and interaction with the environment. Full article

With the advancement of digital technology, serious games have become an essential tool for disseminating and educating individuals about cultural heritage. However, systematic empirical research remains limited with respect to how visual elements influence users’ cognitive and emotional engagement through interactive behaviors. Using the “Macau Historic Centre Science Popularization System” as a case study, this mixed-methods study investigates the mechanisms by which visual elements affect user experience and learning outcomes in digital interactive environments. Eye-tracking data, behavioral logs, questionnaires, and semi-structured interviews from 30 participants were collected to examine the impact of visual elements on cognitive resource allocation and emotional engagement. The results indicate that the game intervention significantly enhanced participants’ retention and comprehension of cultural knowledge. Eye-tracking data showed that props, text boxes, historic buildings, and the architectural light and shadow shows (as incentive feedback elements) had the highest total fixation duration (TFD) and fixation count (FC). Active-interaction visual elements showed a stronger association with emotional arousal and were more likely to elicit high-arousal experiences than passive-interaction elements. The FC of architectural light and shadow shows a positive correlation with positive emotions, immersion, and a sense of accomplishment. Interview findings revealed users’ subjective experiences regarding visual design and narrative immersion. This study proposes an integrated analytical framework linking “visual elements–interaction behaviors–cognition–emotion.” By combining eye-tracking and information dynamics analysis, it enables multidimensional measurement of users’ cognitive processes and emotional responses, providing empirical evidence to inform visual design, interaction mechanisms, and incentive strategies in serious games for cultural heritage. Full article

From past to present, modern additions have continued to transform historic environments. While some argue that contemporary extensions disrupt the integrity of historic buildings, others suggest that the contrast between past and present creates a meaningful architectural dialog. This debate raises a key question: in contrasting compositions, which architectural elements draw more visual attention, the historic or the modern? To address this, a visual attention-based analytical approach is adopted. In this study, eye-tracking-based visual attention analysis is used to examine how viewers perceive the relationship between historical and contemporary architectural elements. Instead of conventional laboratory-based eye-tracking, artificial intelligence-supported visual attention software developed from eye-tracking datasets is employed. Four tools—3M-VAS, EyeQuant, Attention Insight, and Expoze—were used to generate heat maps, gaze sequence maps, hotspots, focus maps, attention distribution diagrams, and saliency predictions. These visualizations enabled both a qualitative and quantitative comparison of viewer focus. The case study is the Military History Museum in Dresden, Germany, known for its widely debated contemporary addition representing an oppositional design approach. The results illustrate which architectural components are visually prioritized, offering insight into how contrasting architectural languages are cognitively perceived in historic settings. Full article

Background: Autism spectrum disorder (ASD) is associated with distinct visual attention patterns that provide insight into underlying social-cognitive mechanisms. Methods: This systematic review (PROSPERO: CRD42023429316), conducted per PRISMA guidelines, synthesizes evidence from 14 peer-reviewed studies using eye-tracking to compare oculomotor strategies in autistic children and typically developing (TD) controls. A comprehensive literature search was conducted in PubMed, Web of Science, and Science Direct up to March 2025. Study inclusion criteria focused on ASD versus TD group comparisons in individuals under 18 years, with key metrics, fixation duration and count, spatial distribution, saccadic parameters systematically extracted. Risk of bias was assessed using the QUADAS-2 tool, revealing high heterogeneity in both index tests and patient selection. Results: The results indicate that autistic children exhibit reduced fixation on socially salient stimuli, atypical saccadic behavior, and more variable spatial exploration compared to controls. Conclusions: These oculomotor differences suggest altered mechanisms of social attention and information processing in ASD. Findings suggest that eye-tracking can contribute valuable information about heterogeneous gaze profiles in ASD, providing preliminary insight that may inform future studies to develop more sensitive diagnostic tools. This review highlights visual attention patterns as promising indicators of neurocognitive functioning in ASD. Full article

Background/Objectives: Mild cognitive impairment (MCI) affects multiple functional and cognitive domains, rendering it challenging to diagnose. Brief mental status exams are insensitive while detailed neuropsychological testing is time-consuming and presents accessibility issues. By contrast, the Oculo-Cognitive Addition Test (OCAT) is a rapid, objective tool that measures oculometric features during mental addition tasks under one minute. This study aims to develop artificial intelligence (AI)-derived predictive models using OCAT eye movement and time-based features for the early detection of those at risk for MCI, requiring more thorough assessment. Methods: The OCAT with integrated eye tracking was completed by 250 patients at the Mayo Clinic Arizona Department of Neurology. Raw gaze data analysis yielded time-related and eye movement features. Random Forest and univariate decision trees were the feature selection methods used to identify predictors of Dementia Rating Scale (DRS) outcomes. Logistic regression (LR) and K-nearest neighbors (KNN) supervised models were trained to classify PMCI using three feature sets: time-only, eye-only, and combined. Results: LR models achieved the highest performance using the combined time and eye movement features, with an accuracy of 0.97, recall of 0.91, and an AUPRC of 0.95. The eye-only and time-only LR models also performed well (accuracy = 0.93), though with slightly lower F1-scores (0.87 and 0.86, respectively). Overall, models leveraging both time and eye movement features consistently outperformed those using individual feature sets. Conclusions: Machine learning models trained on OCAT-derived features can reliably predict DRS outcomes (PASS/FAIL), offering a promising approach for early MCI identification. With further refinement, OCAT has the potential to serve as a practical and scalable cognitive screening tool, suitable for use in clinics, at the bedside, or in remote and resource-limited settings. Full article

We survey recent advances in second-language (L2) Mandarin lexical tones research and show how an interpretable computational approach can deliver parameter-aligned feedback across perception–production (P ↔ P). We synthesize four strands: (A) conventional evaluations and tasks (identification, same–different, imitation/read-aloud) that reveal robust tone-pair asymmetries and early P ↔ P decoupling; (B) physiological and behavioral instrumentation (e.g., EEG, eye-tracking) that clarifies cue weighting and time course; (C) audio-only speech analysis, from classic F0 tracking and MFCC–prosody fusion to CNN/RNN/CTC and self-supervised pipelines; and (D) interpretable learning, including attention and relational models (e.g., graph neural networks, GNNs) opened with explainable AI (XAI). Across strands, evidence converges on tones as time-evolving F0 trajectories, so movement, turning-point timing, and local F0 range are more diagnostic than height alone, and the contrast between Tone 2 (rising) and Tone 3 (dipping/low) remains the persistent difficulty; learners with tonal vs. non-tonal language backgrounds weight these cues differently. Guided by this synthesis, we outline a tool-oriented framework that pairs perception and production on the same items, jointly predicts tone labels and parameter targets, and uses XAI to generate local attributions and counterfactual edits, making feedback classroom-ready. Full article

Background: Body dysmorphic disorder (BDD) is a psychiatric condition characterized by a preoccupation with perceived appearance flaws. It is highly prevalent among aesthetic surgery candidates and can negatively impact surgical outcomes. The Body Dysmorphic Disorder Questionnaire (BDDQ) is used for BDD screening, but objective validation is limited. This study aimed to determine whether individuals screening positive for BDD exhibit different visual perception patterns of their own and model faces compared to controls, using eye-tracking technology. Methods: We conducted a cross-sectional study among 79 participants, including psychiatric patients and medical students. Participants completed the BDDQ and underwent eye-tracking while evaluating standardized photographs of models and their own faces. Gaze fixation patterns were recorded across pre-defined facial areas of interest. Perception and aesthetic assessment differences between the BDDQ-positive and BDDQ-negative groups were studied. Results: Participants focused most frequently on the nose, eyes and eyebrows. Compared to model faces, more attention was directed toward their own chin and cheeks. However, BDDQ screening results did not significantly influence fixation patterns or eye-tracking metrics. Psychiatric patients, regardless of BDDQ status, exhibited more numerous and shorter fixations than students. All participants rated model faces as significantly more attractive (i.e., higher aesthetic rating) than their own, with the largest difference observed in the BDDQ-positive group. Conclusions: While individuals screening positive for BDD reported lower self-attractiveness, eye-tracking patterns did not differ significantly from those of healthy participants. These findings suggest that BDDQ remains a useful screening tool for subjective dissatisfaction but may not correspond to objective differences in facial visual processing. Full article

Driving is a cognitively demanding task engaging attentional effort and working memory resources, which increases cognitive load. The aim of this study was to evaluate the discriminative capabilities of an objective eye tracking method in comparison to a subjective self-report scale (the NASA–Task Load Index) in distinguishing cognitive load levels during driving. Participants (N = 685) performed highway and urban driving in a fixed-base driving simulator. The N-Back test was used as a secondary task to increase cognitive load. In line with previous studies, the NASA–Task Load Index was shown to be an accurate self-report tool in distinguishing conditions with higher and lower levels of cognitive load due to the additional N-Back task, with best average accuracy of 0.81 within the highway driving scenario. Eye gaze metrics worked best when differentiating between stages of highway and urban driving, with an average accuracy of 0.82. Eye gaze entropy measures were the best indicators for cognitive load dynamics, with average accuracy reaching 0.95 for gaze transition entropy in the urban vs. highway comparison. Eye gaze metrics showed significant correlations with the NASA–Task Load Index results in urban driving stages, but not in highway driving. The results demonstrate that eye gaze metrics can be used in combination with self-reports for developing algorithms of cognitive load evaluation and reliable driver state prediction in different road conditions. Full article

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

Background: Clinical practice suggests that objective assessment tools are needed to assess adults with inattention or hyperactivity, informed by the underlying pathophysiology of attention-deficit and hyperactivity disorder (ADHD). This systematic review comprehensively evaluates the current objective assessment methods as an adjunct diagnostic tool for these adults. Methods: We conducted a systematic review of studies investigating various objective diagnostic methods to assess adults with ADHD and healthy controls. The database search occurred from its inception to 23 December 2024. Results: Our search yielded 46 studies that reported on various objective methods to assess adults with ADHD. The MOXO-distracted Continuous Performance Test (MOXO-d-CPT), eye-tracker with MOXO-d CPT, Conners’ Continuous Performance Test—3rd edition (CCPT-3), and oculomotricity can differentiate between true and feigned ADHD or other diagnostic possibilities. The Quantified Behavior Test (Qb Test+) can detect hyperactivity and differentiate it from other psychiatric disorders. Mono-d, CCPT-3, Qb Test+, Test of Variables and Attention (TOVA), Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), and oculomotricity can monitor pharmacotherapy response. Functional near-infrared spectroscopy (fNIRS) offers more promise than structural imaging and demonstrates a moderate level of sensitivity and specificity to differentiate adults with and without ADHD by performing the verbal fluency test. Notwithstanding, electroencephalography (EEG)/event-related potential (ERP) shows potential in diagnosis and treatment monitoring (e.g., neurofeedback training). In addition, transcriptome-based biomarkers have also been explored as diagnostic tools. Conclusion: The diagnosis and monitoring of ADHD in adults come with a unique set of challenges due to psychiatric comorbidity, including depression and anxiety; fluctuation of symptoms over time; and lack of consensus among clinicians and professional organizations to adopt objective tests in the diagnostic process. Our findings support the notion that a combination of clinical assessment and objective biomarkers targeting distinct pathophysiological aspects may enhance the accuracy of ADHD diagnosis. Full article

Feline cryptococcosis is a common systemic mycosis that typically begins in the nasal cavity and can extend to the eyes, skin, and CNS. Objective monitoring during therapy remains limited. This study aimed to test whether serial serum cryptococcal antigen titers (CALAS) and a simple six-domain clinical score can track response, and to describe clinicopathologic trends during amphotericin B and fluconazole treatment. We retrospectively reviewed cases from 2014 to 2023 and analyzed a single-center cohort of 35 cats. Management used amphotericin B plus fluconazole for ~3–4 months, then fluconazole maintenance. Monthly assessments included CALAS (log₂), the clinical score (0–18), and routine hematology/biochemistry; mixed-effects models tested change over time. CALAS fell (mean log₂ 12.00 to 6.79), total leukocytes decreased (16.85 × 10⁹/L to 10.90 × 10⁹/L) with a similar neutrophil decline, alanine aminotransferase remained stable, and blood urea nitrogen/creatinine increased in some cats. The clinical score improved from 3.83 to 0.68. Seven cats developed azotemia during amphotericin B; the drug was stopped, renal values normalized in three, and four remained azotemic at the last follow-up. These findings support the use of CALAS and a simple score as practical monitoring tools and underscore the need for routine renal surveillance. Full article

Background: Early detection of autism spectrum disorder (ASD) is essential, as the first two years of life represent a critical window of neuroplasticity during which timely interventions can improve developmental outcomes. Traditional diagnostic methods, such as ADOS and ADI-R, rely on caregiver reports and structured observations, limiting ecological validity and accessibility. Eye-tracking (ET) offers a non-invasive, scalable approach to assess early atypical gaze patterns. Objectives: This systematic review and meta-analysis synthesized evidence on the diagnostic accuracy of ET for early ASD detection and its potential as an adjunctive screening tool. Methods: A comprehensive search of PubMed, Scopus, Web of Science, Medline, and the Cochrane Library identified studies published between January 2015 and July 2025. Eligible studies evaluated ET in infants and toddlers (≤36 months) for early ASD identification, following PRISMA guidelines. Results: Out of 513 records, 57 studies were included. Most studies reported reduced fixation on social stimuli, atypical gaze following, and preference for geometric over social images in infants later diagnosed with ASD. Pooled effect sizes indicated a moderate-to-large difference between ASD and typically developing groups in social fixation time (Hedges’ g ≈ 0.65, 95% CI: 0.48–0.82, I² = 58%). Studies integrating machine learning algorithms (n = 14) achieved improved sensitivity (up to 89%) and specificity (up to 86%) compared with conventional gaze metrics. Conclusions: Overall, ET shows strong potential as an early adjunctive screening method for ASD. Nonetheless, methodological heterogeneity and lack of standardized protocols currently limit clinical translation, underscoring the need for multi-center validation and task standardization. Full article

Background: Autism spectrum disorder (ASD) and related neurodevelopmental conditions are a significant public health concern, with diagnostic delays hindering timely intervention. Traditional assessments often lead to waiting times exceeding a year. Advances in artificial intelligence (AI) and biomarker-based screening offer objective, efficient alternatives for early identification. Objective: This review synthesizes the latest evidence for AI-enabled technologies aimed at improving early ASD identification. Modalities covered include eye-tracking, acoustic analysis, video- and sensor-based behavioral screening, neuroimaging, molecular/genetic assays, electronic health record prediction, and home-based digital applications or apps. This manuscript critically evaluates their diagnostic accuracy, clinical feasibility, scalability, and implementation hurdles, while highlighting regulatory and ethical considerations. Findings: Across modalities, machine learning approaches demonstrate strong accuracy and specificity in ASD detection. Eye-tracking and voice-acoustic classifiers reliably differentiate for autistic children, while home-video analysis and Electronic Health Record (EHR)-based algorithms show promise for scalable screening. Multimodal integration significantly enhances predictive power. Several tools have received Food and Drug Administration clearance, signaling momentum for wider clinical deployment. Issues persist regarding equity, data privacy, algorithmic bias, and real-world performance. Conclusions: AI-enabled screeners and diagnostic aids have the potential to transform ASD detection and access to early intervention. Integrating these technologies into clinical workflows must safeguard equity, privacy, and clinician oversight. Ongoing longitudinal research and robust regulatory frameworks are essential to ensure these advances benefit diverse populations and deliver meaningful outcomes for children and families. Full article