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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 余家斌(Chia-Pin (Simon) | |
| dc.contributor.author | Léo Gaillard | en |
| dc.contributor.author | 高亞立 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:43:39Z | - |
| dc.date.available | 2017-08-24 | |
| dc.date.copyright | 2017-08-24 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-16 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68948 | - |
| dc.description.abstract | Many psychological studies have shown an effect of environment on human stress, and emphasized that natural environments, on the contrary to urban ones, have positive effects at several levels: cognitive, affective and physiological. Different theories tried to propose a global frame that explains these observations, but a key issue that seems to make them contradict each other is to know if human response facing an environment is first cognitive or affective. However, in all the proposed theories, evolution plays an important role as it gives us tools to deal with information we received, which isn’t possible without establishing preferences. In this study, we try to have a more objective view on the phenomenon, as we hope retrieving any psychological aspect from the reasoning. We consider human a little like a machine, but one shouldn’t be fooled, it is only to understand better our differences. This paper aims to establish a first attempt to understand how the environment, seen as neutral information as long as it has not been treated yet, may hide in its very structure some triggers of stress or even meaning. Can we analyze independently this structure from the receiver that deals with the information? To this end we set up an experiment that tries to determine a complexity effect of the signal on affective and cognitive state. For this experiment we recorded EEG from 40 participants while they were listening to 4 songs of different complexities and doing some simple arithmetical tasks. At the end they were asked to rank the sounds according to their preference. We used several measures of complexity of the stimuli to see more in detail how it would impact the EEG, the participants’ results and the song they preferred. We got significant results for the preference ratings but not for the tasks results. Because our way of recording EEG was a little inadequate, it was difficult to draw any definitive conclusion out of the data but we presented a lot of techniques to be applied in further studies and drew some hypothesis from the literature. In this respect, our paper doesn’t have any other claim that proposing a general frame and hypothesis that link stress recovery and predictive coding, before a stronger validation from robust EEG data. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T02:43:39Z (GMT). No. of bitstreams: 1 ntu-106-R03625059-1.pdf: 12652389 bytes, checksum: f4fa511c6c3bd562b9483d7cf671e8ff (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | Acknowledgment i
Abstract iii Table of Contents v List of Figures p.1 List of Tables p.4 Chapter 1 Introduction p.5 1.1 General Introduction p.5 1.2 Research Questions p.10 1.3 Research Process p.10 Chapter 2 Literature Review p.10 2.1 Stress and Environments p.10 2.1.1 Theoretical Approach p.10 2.1.2 Measuring Approach p.18 2.1.2.1 Measuring Restoration p.19 2.1.2.1.1 Shema of Experiments p.19 2.1.2.1.2 Provoke the Stressor p.20 2.1.2.1.3 Questionnaire p.20 2.1.2.1.4 Physiological Measurements p.21 2.1.2.2 Being Aware of Bias p.22 2.1.2.2.1 Concerning Participants p.22 2.1.2.2.2 Concerning the Displayed Environments p.23 2.1.2.3 Discussion p.24 2 2.1.2.3.1 Parameters which Impact Restoration p.24 2.1.2.3.2 Where in the Brain? p.27 2.1.2.3.3 Focus on Audio Only p.29 2.1.3 Conclusion p.30 2.2 Predictive Coding: a General Frame to Understand the Brain p.31 2.2.1 Bayesian Brain and Predictive Coding p.31 2.2.1.1 Universal Computation and a Priori from Dataset p.31 2.2.1.2 Bayesian Inferences and Predictive Coding Theory p.36 2.2.1.3 Evidences of Such Calculation in Experiments p.38 2.2.1.4 Learning from Errors p.39 2.2.1.5 The Role of Attention p.40 2.2.1.6 Combining Different Levels p.44 2.2.1.7 A word about the “Free Energy Principle” p.45 2.2.2 Mismatch Negativity, PEs and Other Brain Responses p.46 2.2.2.1 Observed Waveforms after a Deviant p.46 2.2.2.2 Explaining MMN and these Observed Waveforms p.50 2.2.3 Zoom on Audio p.52 2.2.3.1 Single, Double and Triple “Simple” Deviants p.52 2.2.3.2 Deeper Levels p.54 2.2.3.3 Focus on Music p.56 2.2.3.4 About Cognitive and Affective Responses p.58 2.2.3.5 Conclusion p.60 Chapter 3 Research Methodology p.62 3.1 Environment as Information p.62 3.1.1 Approach p.62 3.1.2 Four Characteristics of a Signal as Carrying Information p.68 3.1.2.1 Duration p.68 3.1.2.2 Energy p.69 3.1.2.3 Entropy p.71 3.1.2.4 Complexity/Predictability p.73 3.1.2.4.1 Variogram Approach p.74 3.1.2.4.1.1 Simple Formula p.74 3.1.2.4.1.2 Another Use of Variogram p.77 3.1.2.4.2 Algorithm Complexity p.80 3.1.2.4.3 Fractals p.81 3.1.2.4.3.1 For a Time Series p.81 3.1.2.4.3.2 For an Image p.83 3.1.2.4.4 Entropy Approach p.85 3.1.2.4.4.1 Approximate Entropy and Sample Entropy p.85 3.1.2.4.4.2 Permutation Entropy p.87 3.1.2.4.4.3 Other Use p.88 3.1.2.4.4.4 2D predictability p.95 3.1.2.4.5 Dynamical Analysis; Global and Local Predictability p.98 3.1.2.5 Complements p.102 3.1.3 Stimulus Complexity VS EEG Complexity p.104 3.2 Experiment p.106 3.2.1 Design p.106 3.2.2 Sound Complexity p.115 3.3 Summary and Hypothesis p.121 Chapter 4 Results and Discussion p.122 4.1 Preference and Tasks Results p.122 4.2 EEG Analysis p.133 4.2.1 A Quick View on the Data p.133 4.2.2 Analysis p.139 4.2.2.1 Local Study p.142 4.2.2.1.1 Peaks p.142 4.2.2.1.2 Shape of the Response p.144 4.2.2.2 Global Study p.146 Chapter 5 Conclusion p.153 References p.156 Appendix p.179 Appendix A-Waveforms likely to be observed after a deviant p.179 Appendix B- Math task answer sheet p.180 Appendix C- Analysis of the complexity of the 4 sounds with several methods. p.183 Appendix D- Complexity of EEG and energy of EEG of several frequency bands p.194 Appendix E- Spectrogram of several environments sounds p.200 | |
| dc.language.iso | en | |
| dc.title | 聲音療癒特性的評估 | zh_TW |
| dc.title | Assessment of the restorative properties of sounds | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 袁孝維(Hsiao-Wei Yuan),劉奇璋(Chi-Chang Liu) | |
| dc.subject.keyword | stress recovery,predictive coding,ART,SRT,auditory perception,signal complexity,MMN,nature and urban environments, | en |
| dc.relation.page | 204 | |
| dc.identifier.doi | 10.6342/NTU201700773 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-16 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 森林環境暨資源學研究所 | zh_TW |
| 顯示於系所單位： | 森林環境暨資源學系 | |
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