{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:27:06Z","timestamp":1786512426546,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["22YJCZH240"],"award-info":[{"award-number":["22YJCZH240"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["CCNU26KYZHSY20"],"award-info":[{"award-number":["CCNU26KYZHSY20"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["CCNU26ZH017"],"award-info":[{"award-number":["CCNU26ZH017"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Eye Mov. Res.","JEMR"],"abstract":"<jats:p>Children with autism spectrum disorder (ASD) often exhibit atypical patterns of visual attention allocation and social-cue processing. Eye-tracking scanpath (ETSP) retains information about fixation points, saccade paths and their temporal changes in the form of images, providing an intuitive and computable data representation for analyzing ASD-related visual attention patterns. However, in ASD auxiliary identification studies, the same participant often generates multiple eye-tracking recordings or multiple visual representation samples. If participant independence is not properly considered during model evaluation, the training and test sets may share individualized eye-movement patterns from the same child. In such cases, the model may learn subject-specific characteristics rather than stable and transferable ASD-related visual attention features, leading to an overestimation of its recognition ability on unseen participants. To address this issue, we propose a Global\u2013Local Collaborative Fusion Network (GLCF-Net) under a strict participant-independent splitting protocol. Specifically, the proposed method first maps ETSP images into patch token sequences through a shared Patch Embedding layer. A CNN-based local branch is then used to extract local trajectory morphology, path density, and spatial neighborhood structure, while a ViT-based global branch models cross-region gaze transitions and the overall attention distribution. Finally, a gated adaptive fusion module dynamically integrates local and global information to enhance the representation of stable visual attention features. In the primary repeated stratified five-fold participant-level evaluation, averaging the two out-of-fold probabilities for each participant yielded an Accuracy of 87.0% and a ROC-AUC of 93.7%; the original participant split, retained as a secondary analysis, yielded an Accuracy of 83.52% and a ROC-AUC of 90.27%. Under the reported frozen-backbone configurations, the model also showed a balanced pattern across Accuracy, Recall, and F1-score. These results characterize performance for unseen participants within the same dataset and acquisition conditions.<\/jats:p>","DOI":"10.3390\/jemr19040085","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T10:23:50Z","timestamp":1786357430000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Participant-Independent Classification of Autism-Related Visual Attention Patterns from Eye-Tracking Scanpath Images Using a Global\u2013Local Fusion Network"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3920-3705","authenticated-orcid":false,"given":"Kun","family":"Zhang","sequence":"first","affiliation":[{"name":"National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"},{"name":"National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2977-2907","authenticated-orcid":false,"given":"Junling","family":"Kong","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"},{"name":"National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junhui","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"},{"name":"National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"},{"name":"National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingying","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"},{"name":"National Engineering Research Center of Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1186\/s40001-024-01916-2","article-title":"New advances in the diagnosis and treatment of autism spectrum disorders","volume":"29","author":"Qin","year":"2024","journal-title":"Eur. 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