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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張俊彥 | zh_TW |
dc.contributor.advisor | Chun-Yen Chang | en |
dc.contributor.author | 董聿馨 | zh_TW |
dc.contributor.author | Yu-Hsin Tung | en |
dc.date.accessioned | 2025-04-24T16:05:01Z | - |
dc.date.available | 2025-04-25 | - |
dc.date.copyright | 2025-04-24 | - |
dc.date.issued | 2025 | - |
dc.date.submitted | 2025-03-31 | - |
dc.identifier.citation | Aoki, Y. (1999). Trends in the study of the psychological evaluation of landscape. Landscape Research, 24(1), 85-94.
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Scientific Reports, 10(1), 20774. Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., & Xing, E. (2023). Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in neural information processing systems, 36, 46595-46623. Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2017). Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence, 40(6), 1452-1464. Zube, E. H., Sell, J. L., & Taylor, J. G. (1982). Landscape perception: research, application and theory. Landscape Planning, 9(1), 1-33. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97310 | - |
dc.description.abstract | 本研究探討人們對自然環境的景觀偏好,結合認知科學與深度學習模型分析景觀偏好矩陣神經基礎。研究比較了人們對於偏好矩陣的行為評分(包括一致性、易讀性、複雜性、神秘性和整體偏好)與大腦特定功能區域的活化,以及使用深度學習模型的特徵來預測偏好矩陣的相關指標。研究結果顯示,不同景觀偏好指標涉及不同的的腦神經機制,高階認知區域與一致性和易讀性相關,而複雜性和神秘感則與場景識別及記憶相關區域呈現較高的相關性。此外,整體偏好似乎是多層次視覺訊息的整合結果,無法單獨由AI模型進行預測。本研究初探了人類對於景觀偏好的神經機制,並對環境心理學、景觀設計及機器學習用於景觀偏好的預測提供實證支持及應用價值。 | zh_TW |
dc.description.abstract | Landscape preference is shaped by a complex interplay of perceptual, cognitive, and affective processes. This study investigates the behavioral, neural, and computational mechanisms underlying landscape preference. By integrating human behavioral ratings, functional magnetic resonance imaging (fMRI) data, and deep learning-based scene analysis. Participants rated natural landscapes based on coherence, complexity, mystery, legibility, and overall preference while undergoing fMRI scanning. The BOLD activation of preference indicators and representational similarity (RSA) was applied to examine the cognitive correlation of landscape preference and alignment between brain activation patterns, human aesthetic judgments, and feature representations from a deep neural network. The results revealed distinct neural correlates for different aesthetic dimensions. Coherence and legibility engaged higher cognitive regions involved in spatial organization and decision-making, while complexity and mystery were associated with brain areas related to emotional processing and memory retrieval. The RSA findings indicated that mid-to-late layers of AlexNet captured human perceptions of complexity and legibility, but preference remained difficult to predict, suggesting that aesthetic appreciation extends beyond visual processing to include cognitive and emotional factors. These findings offer insights into the neural basis of landscape aesthetics and the extent to which AI models approximate human aesthetic judgments. While deep learning models capture certain visual features, they do not fully account for the subjective and affective dimensions of preference. | en |
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dc.description.tableofcontents | Table of Contents
Dedication I Abstract II 中文摘要 III Table of Contents IV List of Figures VIII List of Tables XI 1 Introduction 1 1.1 Background 1 1.1.1 Bridging Perceptual and Computational Approaches 2 1.1.2 AI and Machine Learning: A New Paradigm for Landscape Assessment 2 1.2 Research Objectives 4 1.3 Scope and Contributions of the Study 6 1.3.1 Scope of the Study 6 1.3.2 Contributions of the Study 7 1.4 Overview of the Dissertation 8 2 Literature Review 10 2.1 The Subjective Framework to Preference Assessment 12 2.1.1 Theoretical Foundations 12 2.1.2 Landscape Preference Matrix: A Cognitive Model of Aesthetic Evaluation 14 2.1.3 The Empirical Research to Landscape Preference Assessment 17 2.2 The Objective Framework to Landscape Preference 18 2.2.1 Theoretical Foundations 19 2.2.2 The Visual Features of Landscape Perception 20 2.2.3 Integrating Global and Local Features 23 2.3 The Cognitive Framework to Preference Assessment 24 2.3.1 Scene Recognition and Spatial Processing 24 2.3.2 The Reward Pathway and Aesthetic Preference 27 2.3.3 Empirical Evidence on Scene Preference 31 2.4 The Data-Driven Approach to Preference Assessment 34 2.4.1 Neural Networks and the Convolutional Process 36 2.4.2 Deep Neural Networks and Scene Assessment 36 2.4.3 Large Language Models and Scene Assessment 38 2.4.4 Database in Landscape Aesthetic Research 39 2.5 Research Gap and Needs 41 2.5.1 Challenges in Landscape Preference 41 2.5.2 Research Gap: Need for an Integrated Approach 41 2.5.3 Establishing The Research Frameworks 43 3 Methods 44 3.1 Landscape Stimuli 45 3.1.1 Filtering and Processing of Landscape Images 46 3.2 Phase I: Qualitative Survey on Landscape Preference and Visual Features 49 3.2.1 Experiment I (qualitative survey) 49 3.2.2 Participants and ethics 50 3.2.3 Content Analysis and ATLAS.ti Protocol 51 3.3 Phase II: fMRI Study on Neural Correlates of Landscape Preference 54 3.3.1 Experiment II (fMRI experiment) 54 3.3.2 Participants and ethics 57 3.3.3 fMRI data analysis 58 3.4 Phase III: AI-Based Prediction 60 3.4.1 Behavioral data analysis 60 3.4.2 Brain Regions of Interest Analysis 60 3.4.3 Model Utility, Feature Extraction, and Representational Similarity Analysis (RSA) 63 3.4.4 Representational Similarity Analysis 65 4 Result 67 4.1 Phase I: Qualitative Result 67 2.1.1 Co-occurrence analysis 68 4.2 Phase II: Psychological Results 69 4.2.1 Behavioral Result 70 4.2.2 Cognitive result 74 4.3 Phase III: AI Model Prediction and Representational Similarity to Human Result 94 4.3.1 DRM of Behavioral Preference 95 4.3.2 DRM of Brain ROI 97 4.3.3 DRM of AlexNet Model 100 4.3.4 RSA Comparing Human Ratings and Brain ROI Activation 101 4.3.5 RSA Comparing behavioral and AI-model relationship 102 5 Discussion 105 5.1 Qualitative Exploration of Visual Feature and Landscape Preference Matrix 106 5.2 Behavioral and Neural Correlates of Landscape Preference 107 5.2.1 Neural Correlates of Mystery Perception 108 5.2.2 Neural Correlates of Complexity Perception 108 5.2.3 Neural Correlates of Coherence Perception 109 5.2.4 Neural Correlates of Legibility Perception 109 5.2.5 Neural Correlates of Overall Preference 109 5.3 AI-Based Landscape Preference Prediction 110 5.3.1 Brain ROI and Behavioral Response 110 5.3.2 AlexNet and Behavioral Response Prediction 111 5.4 Research Implication and Limitation 113 5.4.1 Research Implications 113 5.4.2 Limitations 121 5.5 Suggestion for Further Research 123 6 Conclusion 124 7 Acknowledgments 127 8 Reference 128 9 Supporting Information 137 9.1 Descriptive Data of Preference Rating in Phase I 137 9.2 The accuracy comparison of different statistic methods to predict preference matrix in AlexNet in Phase III 139 9.3 The Research Ethics Committee Approvals for Study Phase II, III 141 9.4 Research Ethics Committee Approvals for Study Phase I 142 10 Appendix A: Key References on Neuroimaging, Aesthetic Perception, and Landscape Preferences 143 List of Figures Figure 1. The illustration of research investigating and modeling the relationship between visual scenes and aesthetic perception. 3 Figure 2. The illustration of research investigating and modeling the relationship between visual scenes and aesthetic perception. 5 Figure 3. Existing Conceptual Framework of Landscape Quality Assessment Approaches 11 Figure 4. The Visual Pathways of Scene Recognition. 25 Figure 5. The visual aesthetic process correlated with visual object perception, scene recognition, and related areas of the ventral pathway. 27 Figure 6. The Neural Correlates of the Reward Pathway and Aesthetic Judgment. 30 Figure 7. Hierarchical Relationship of AI, ML, DNN, CNN, and LLM 35 Figure 8. AlexNet Architecture 37 Figure 9. Research framework. 43 Figure 10. Research hypothesis. 44 Figure 11. Selected Images from the Places365 Dataset regarding Water, Mountain, and Forest with Subsets. 45 Figure 12. The illustration of filtering criteria and the protocol of constructing the landscape scene dataset. 46 Figure 13. Image Selection Process for Landscape Stimul 48 Figure 14. The procedure of the online open-ended scene questionnaire. 50 Figure 15. Content Analysis Workflow for Open-Ended Responses 51 Figure 16. Codebook for Content Analysis of Landscape Preference 53 Figure 17. The illustration of landscape aesthetic scene rating task in an fMRI experiment. 54 Figure 18. fMRI Experiment Questions for Landscape Preference Assessment 56 Figure 19. Landscape Scene Stimuli and Control Stimuli 56 Figure 20. fMRI Scanning Process and Brain Imaging 57 Figure 21. fMRI Data Preprocessing and Motion Correction 58 Figure 22. Participants conducting the fMRI experiment 59 Figure 23. Conceptual Framework of AI Prediction, Behavioral Response, and Neural Activation 66 Figure 24. Co-occurrence analysis of local and global features across four perceptual dimensions in the Landscape Preference Matrix. 68 Figure 25. The key result figure of MRI psychological key findings. 73 Figure 26. Brain Activations Associated with Mystery Perception in Natural Scenes. 75 Figure 27. The result of activated areas on brain upon Complexity Perception. 77 Figure 28. The result of activated areas on the brain upon thinking Legibility. 81 Figure 29. The result of activated areas on brain upon Legibility Perception in Mountain scene. 84 Figure 30. The result of activated areas on brain upon Legibility Perception in Forest scene. 86 Figure 31. The result of activated areas on brain upon Overall Preference in Water scene. 89 Figure 32. The result of Activated Areas on the Brain upon Overall Preference in Mountain Scene. 90 Figure 33. The Visualization of Activated Areas on the Brain upon Overall Preference in Forest Scene. 92 Figure 34. Summary of activated brain regions across forest, water, and mountain landscapes 93 Figure 35. RDMs of Human Behavioral Ratings for Landscape Preferences Across 30 Scenes. 94 Figure 36. RDMs of Human Behavioral Ratings for Landscape Preferences Across Three Categories. 95 Figure 37. RDMs of Human Behavioral Ratings for Landscape Preferences Across 30 Scenes. 96 Figure 38. The overall results of each RDM correlation heatmap when comparing three landscape categories. 99 Figure 39. RDMs of AlexNet ReLU layers for 30 scene images 100 Figure 40. The RSA Between Human Behavioral Ratings and Brain ROI Activation. 102 Figure 41. RSA heat map correlation between AlexNet ReLU layers and landscape preference dimensions. 104 Figure 42. Integrated contributions of three studies combining visual features, fMRI evidence, and AI predictions. 112 List of Tables Table 1. Theories related to the research and its brief description. 13 Table 2. Landscape Preference Matrix and description from Kaplan & Kaplan. 14 Table 3. The dimensions of Preference Matrix from Kaplan and Kaplan (1989). 15 Table 4. Theoretical Frameworks on Feature Processing in Visual Perception. 20 Table 5. Illustrating key visual features in landscape assessment into global and local features, 23 Table 6. The brain area is related to scene recognition and understanding. 26 Table 7. The brain area is related to object recognition and the affective-related regions. 29 Table 8. The comparison of scene aesthetic dataset regarding the factors and aesthetic data. 40 Table 9. Filtering Process and Remaining Categories. 47 Table 10. Table summarizing the hypothesis on the connection between local and global visual features and the Landscape Preference Matrix dimensions. 49 Table 11. Codebook for Content Analysis of Landscape Preference 52 Table 12. Landscape Preference Matrix questionnaire and the definition. 55 Table 13. Mapping Brain Regions to Brodmann Areas 62 Table 14. Descriptive statistics of preference ratings for different landscape types (5-point Likert scale). 67 Table 15. Mean and Standard Deviations for Mystery, Complexity, Coherence, Legibility, and Overall Preference for Different Landscape Categories. 69 Table 16. Mystery Ratings for Three Landscape Categories 70 Table 17. Scene Complexity Results for Three Landscape Categories. 71 Table 18. Scene Coherence Ratings for Three Landscape Categories. 71 Table 19. Scene Legibility Ratings for Three Landscape Categories. 72 Table 20. Scene Preference Ratings for Three Landscape Categories. 72 Table 21. Brain Activations Associated with Mystery Perception in Natural Scenes. 74 Table 22. Brain Activation Associate with Complexity Perception in Natural Scene. 76 Table 23. Brain Activation Associate with Coherence Perception in Natural Scene. 78 Table 24. Brain Activation Associate with Water Scene on Legibility Perception. 80 Table 25. Brain Activation Associate with Mountain Scene in Legibility Perception. 83 Table 26. Brain Activation Associate with Legibility in Forest Scene. 85 Table 27. Brain Activations Associated with Water in Overall Preference. 88 Table 28. Brain Activations Associated with Overall Preference in Mountain Scene. 89 Table 29. Brain Activation Associated with Overall Preference in Forest Scene. 91 Table 30. The preference score for each landscape scene in the qualitative questionnaire. 137 | - |
dc.language.iso | en | - |
dc.title | 探討景觀視覺特徴對美感體驗與認知的影響 | zh_TW |
dc.title | To Investigate the Relationship Between Landscape Visual Features on Aesthetic Perception and Cognition | en |
dc.type | Thesis | - |
dc.date.schoolyear | 113-2 | - |
dc.description.degree | 博士 | - |
dc.contributor.oralexamcommittee | 威廉蘇利文;歐聖榮;陳建中;何立智;李英弘;鄭佳昆 | zh_TW |
dc.contributor.oralexamcommittee | William Sullivan;Sheng-Jung Ou;Chien-Chung Chen;Li-Chih Ho;Ying-Hung Li ;Chia-Kuen Cheng | en |
dc.subject.keyword | 景觀美學,功能性磁振造影,偏好預測模型,視覺知覺, | zh_TW |
dc.subject.keyword | Landscape aesthetics,fMRI,deep learning,visual perception, | en |
dc.relation.page | 160 | - |
dc.identifier.doi | 10.6342/NTU202500791 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2025-04-01 | - |
dc.contributor.author-college | 生物資源暨農學院 | - |
dc.contributor.author-dept | 園藝暨景觀學系 | - |
dc.date.embargo-lift | N/A | - |
顯示於系所單位: | 園藝暨景觀學系 |
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