[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120919-en":3,"doc-seo-120919-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120919,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Computational Modeling of Animal Behavior in T-mazes - Insights from Machine Learning","This study investigates animal decision-making intricacies in T-maze environments using an integrated framework of computational modeling and machine learning. It analyzes binary path choice behavior and develops a mathematical model for fish decision-making, providing analytical explanations of behavioral structure. Machine learning methods—including SVM, KNN, and a PCA-based dimensionality reduction with SVM—are applied to zebrafish and rat behavioral datasets. The approach yields high predictive accuracies (about 98.07% for zebrafish and 98.15% for rats). Results demonstrate the effectiveness of computational tools for decoding animal behavior. ","Ecological Informatics 81 (2024) 102639  \nContents lists available at ScienceDirect  \nEcological Informatics  \njournal [homepage: www.elsevier.com/locate/ecolinf](homepage: www.elsevier.com/locate/ecolinf)  \n| Computational modeling of animal behavior in T-mazes: Insights from machine learning\u003Cbr>Ali Turaba, d, Wutiphol Sintunavaratb, *, Farhan Ullaha, Shujaat Ali Zaidic, Andr´es Montoyo d, **, Josu´e-Antonio Nescolarde-Selva e\u003Cbr>a School of Software, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an 710072, China\u003Cbr>b Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University Rangsit Center, Pathum Thani 12120, Thailand c Department of Computer Science, Faculty of Science, Chiang Mai University, Thailand\u003Cbr>d Department of Software and Computing Systems, University of Alicante, Alicante, Spain e Department of Applied Mathematics, University of Alicante, Alicante, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| MSC (2020): 92D50 00A71\u003Cbr>68U07\u003Cbr>91B06\u003Cbr>47H10\u003Cbr>Keywords: Animal behavior Decision-making T-mazes\u003Cbr>Computational modeling Solution\u003Cbr>Machine learning methods |  | This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decisionmaking process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science. |\n\n1. Introduction  \nThe study of animal decision-making has significantly evolved by incorporating high-resolution tracking systems and computational tools. These innovations have transformed our analysis of spatial navigation and decision-making within controlled environments, such as T-mazes, which offer a structured setting to scrutinize animal cognitive mechanisms (see (Deacon and Rawlins, 2006a; Deacon and Rawlins, 2006b; Sih et al., 2004)). While empirical data provides a foundational understanding of behavior, recent research integrates these observations with computational models to elucidate the complexities of decision-making (see (d’Isa et al., 2021; Ferrarini and Gustin, 2022; Wang, 2019)). The current study continues this interdisciplinary approach, combining machine learning and mathematical modeling to dissect the decisionmaking processes observed in T-maze experiments.  \nFurthermore, in behavioral ecology, animals navigate many decisions related to survival and reproduction, each reflecting the complex interaction between their internal states and external pressures. Investigating these decisions, from binary choices to complex, contextdependent ones, has expanded our comprehension of cognitive capacities, challenging preconceived notions of animal behavior (see (Johnson and Redish, 2007; Preuschoff et al., 2013; Tolman, 1948)). The fusion of mathematical modeling and machine learning in this research offers a dual advantage—enhancing the clarity and specificity of hypotheses while leveraging computational power to identify patterns in ","cbCaicAerawtiVmr","https://ap.wps.com/l/cbCaicAerawtiVmr","pdf",5488928,1,16,"English","en",105,"# Introduction\n## Animal decision-making and T-mazes\n## Mathematical modeling and machine learning integration","[{\"question\":\"What is the core research focus of the study?\",\"answer\":\"The study focuses on animal decision-making in T-maze environments, especially binary choices between two paths, using computational modeling combined with machine learning.\"},{\"question\":\"Which machine learning methods are used for analyzing behavioral data?\",\"answer\":\"Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid PCA dimensionality-reduction approach followed by SVM are used to analyze behavioral datasets from zebrafish and rats.\"},{\"question\":\"How accurate are the predictive results for zebrafish and rats?\",\"answer\":\"The model achieves high predictive accuracy, approximately 98.07% for zebrafish and 98.15% for rats.\"}]","Computational Modeling of Animal Behavior in T-mazes - Insights from Machine Learning | PDF",1785732688,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"computational-modeling-of-animal-behavior-in-t-mazes-insights-from-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/computational-modeling-of-animal-behavior-in-t-mazes-insights-from-machine-learning/120919/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the core research focus of the study?","Question",{"text":75,"@type":76},"The study focuses on animal decision-making in T-maze environments, especially binary choices between two paths, using computational modeling combined with machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used for analyzing behavioral data?",{"text":80,"@type":76},"Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid PCA dimensionality-reduction approach followed by SVM are used to analyze behavioral datasets from zebrafish and rats.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the predictive results for zebrafish and rats?",{"text":84,"@type":76},"The model achieves high predictive accuracy, approximately 98.07% for zebrafish and 98.15% for rats.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]