[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123522-en":3,"doc-seo-123522-105":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},123522,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Understanding Plant Movement - From Kinematics to Machine Learning","Despite appearing immobile, plants can move and may express goal-directed actions that have renewed debate about their cognitive potential. The thesis investigates motor cognition in plant behavior by combining kinematic analysis with machine learning, focusing on decision-making that weighs costs and benefits across contexts. It reviews motor cognition and physiological mechanisms of movement, presents experiments on support-searching in climbing plants, and applies anomaly detection, supervised learning, and ensemble methods to reveal subtle movement programming and circumnutation decision complexity.","Head Office: Università degli Studi di Padova  \nDepartment of General Psychology  \nPh.D. COURSE IN: Psychological Sciences  \nCYCLE: XXXVI  \nUnderstanding Plant Movement:  \nFrom Kinematics to Machine Learning  \nCoordinator: Prof. Lucia Regolin  \nSupervisor: Prof. Umberto Castiello  \nCo-Supervisor: Prof. Gian Antonio Susto  \nPh.D. student: Qiuran Wang  \nTo  \nthe living beings  \nSYNOPSIS  \nDespite their seemingly immobile nature, plants can move, a fact that has often gone overlooked. Recent discoveries have begun to furnish compelling evidence that plants are not passive organisms. They are capable of harboring intentions and translating them into goal-directed actions. This emerging evidence has sparked a lively discussion regarding the cognitive potential of plants. One innovative perspective for the study of plant behavior lies in examining it through the theoretical framework of motor cognition, which posits that cognition is fundamentally intertwined with action. My thesis is focused on investigating motor cognition in plant behavior through a combination of kinematic analysis and machine learning techniques. In particular, I dedicated myself to understanding decisionmaking, which entails the evaluation of costs and benefits associated with actions carried out under different contexts.  \nI shall begin this path by providing an in-depth exploration of motor cognition, and following this concept, behavior will be discussed as intentional by analyzing its final goal. Recent evidence that demonstrates goal-directed behavior in plants will be presented, and the possibility of these actions being intentional will be discussed (Chapter 1) . This discussion highlights the “goal-directedness” of plant movement and sets the stage for my  \ninvestigation of motor intentions in the plant domain. Given the absence of a designated structural basis for cognition in plants, this exploration offers an alternative perspective on how cognitive processes may manifest. In doing so, the thesis will provide alternative theoretical frameworks –– post-cognitivist theories –– for studying plants that move beyond neurocentric paradigms. All of these theories will be used to interpret the experimental findings and addressed in the final discussion.  \nThe thesis will move forward to the topic of plant movement. In Chapter 2, I will introduce the physiological mechanisms that drive plant movement. Several types of plant movements will be discussed, which serve as the foundation for witnessing the presence of movements in plants. In particular, I shall focus on a universal movement exhibited by different organs in climbing plants –– circumnutation.  \nThe theoretical backdrop of motor intention and plant movements paves the way for a detailed examination of the experiments conducted. Given the relatively nascent nature of the study of plant movements, Chapter 3 will be dedicated to introducing common methods employed for this purpose, including time-lapse photography and 3D motion analysis. Additionally, the thesis will introduce the potential of machine learning for understanding plant movements, setting the stage for my subsequent experiments.  \nAfter providing the theoretical background, in Chapter 4, I will highlight the aim of my research, grounded in two goals: first, to examine the motor intentions underlying plant support-searching and their associated decision-making processes; second, to enrich our approaches to understanding plants through the innovative application of machine learning. Then, in Chapter 5, I will describe the general methods employed.  \nStarting from Chapter 6, the experiments will be presented. The first study focused on the adaptive behavior of pea plants when faced with the presence/absence of a potential support. The results demonstrated not only that plants can discern the presence or absence of a support in the environment, but also that they can program their movements accordingly.  \nIn Chapters 7 and 8, I shall report on","cbCaii67vwqE3DTn","https://ap.wps.com/l/cbCaii67vwqE3DTn","pdf",31433756,1,242,"English","en",105,"# Synopsis\n## Motor cognition and intentional plant behavior\n## Plant movement mechanisms and circumnutation\n## Experimental methods for plant movement\n## Research aims and methods\n## Experiments on support-searching decisions\n## Machine learning analysis of behavioral data\n## Discussion: sensory mechanisms and signaling pathways","[{\"question\":\"What central question does the thesis address about plants?\",\"answer\":\"It examines whether plant movement reflects motor cognition and intentional, goal-directed actions rather than passive responses.\"},{\"question\":\"How does the thesis study decision-making in plant support-searching?\",\"answer\":\"It links costs and benefits across contexts to kinematic outcomes, using experiments that test how plants program movements when supports are present or absent.\"},{\"question\":\"Which machine learning approaches are used to analyze plant behavioral data?\",\"answer\":\"The thesis applies unsupervised anomaly detection, supervised machine learning, and ensemble learning to identify patterns in circumnutation and predict growth across conditions.\"}]","Understanding Plant Movement - 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