何種情緒能夠影響人們對藝術品或觀賞物的美醜感受這一議題，在近約 10 年來一直是實徵美學領域裡關注的面向。相關研究的結果顯得非常多樣化，有些學者認為是正向情緒，例如溫暖、快樂，能提升觀賞者對眼前知覺物的喜愛；但有另一派學者卻指向負向情緒，例如恐懼，才能使觀賞者覺得眼前的物件更美。為了更加瞭解此現象，我們統整並參考了相關研究的實驗設計，除原有的情緒因子外，也將不同的情緒刺激呈現長度納入實驗，並在行為測量外，嘗試以參與者的腦波來了解美感偏好，來比較兩者間的異同。評分資料結果顯示，當情緒圖片可被參與者感知的情況下，處在正向情緒中的參與者會更加喜好目標圖片；厭惡情緒會使得參與者較不喜愛目標圖片；恐懼情緒則不影響參與者的偏好。不過，以k近鄰演算法(kNN)訓練出的腦波美感判斷模型的準確率未及預期，最高僅達63.84%，無法與行為資料相互比較。但是，我們仍相信至少在外顯行為上，對於非美術專業人士來說，處在正向情緒才能提升他們對知覺物或藝術品的喜好。

What kind of emotion contributes to affecting people’s aesthetic judgment of liking for perceived object? This issue has been focused on in empirical aesthetics for approximately ten years. Results are inconclusive, for example, some studies suggested that positive emotions, such as pleasure or joy, improve the liking. On the other hand, other studies indicated that negative emotions, such as fear, enhanced beholders’ judgment for artworks. To clarify various findings, we employed past experimental paradigms and included different presenting time of IAPS as a new factor in addition to the emotion factor. Also, we tried to measure the aesthetic preference by behavioral rating as well as by brainwaves, to see whether they would consistent with each other. The findings showed that if IAPS are perceivable, participants liked target pictures more in the positive condition, but their preferences were lower after disgusting emotions are elicited, and the fear did not affect ratings. Unfortunately, the highest accuracy of EEG-based preference classifier trained by k-nearest neighbors algorithm(kNN) was only to 63.84%, which failed to achieve the researcher’s aim, thus the consistency remained in doubt. However, our results implied that positive emotions are beneficial for lay people to appreciate perceived objects or art.

第一章 緒論 1
一、 研究動機 1
二、 研究目的 3
第二章 文獻回顧 5
一、 美感經驗的內涵 5
二、 情緒與美感情緒、美感判斷 11
三、 情緒如何影響美感判斷？體現認知的觀點 18
四、 相關研究的進展 20
五、 美感判斷的測量方式 22
六、 結語 27
七、 研究問題與假設 29
第三章 研究方法 31
一、 研究對象 31
二、 研究工具 31
三、 問卷、腦波美感判斷模型，以及正式實驗實施程序 36
四、 資料處理與統計分析方法 40
第四章 結果 49
一、 第一階段：問卷結果 49
二、 第二階段：腦波美感判斷模型建立結果 50
三、 第三階段：正式實驗結果 53
第五章 討論與建議 63
一、 在行為與腦波上，情緒是否會影響美感判斷？ 64
二、 在行為與腦波上，不同的刺激呈現長度是否會影響美感判斷？ 66
三、 在行為與腦波上，情緒的影響力是否會隨刺激呈現長度改變？ 67
四、 在行為與腦波間，情緒影響美感判斷的方式是否一致？ 68
五、 結論 72
六、 未來研究的建議方向 74
參考文獻 77
附錄一 機器學習訓練結果 86
附錄二 維也納藝術知識與興趣問卷(VAIAK) 91
附錄三 研究工具使用同意書 97
一、 維也納藝術興趣與知識問卷(VAIAK) 97
二、 黑白抽象圖片 98
附錄四 研究倫理審查證書 99
附錄五 研究參與者知情同意書 103
一、 第一階段 103
二、 第二階段 106
三、 第三階段 111

葉浩生. (2010). 具身認知 : 認知心理學的新取向. 心理科學進展, 18(5), 705–710.
Adolphs, R., Damasio, H., Tranel, D., Cooper, G., &Damasio, A. R. (2000). A Role for Somatosensory Cortices in the Visual Recognition of Emotion as Revealed by Three-Dimensional Lesion Mapping. J Neurosci, 20(7), 2683–2690.
Alnuman, N., Al-Nasser, S., &Yasin, O. (2020). Classification of Products Preference from EEG Signals using SVM Classifier. ICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering, 174–179. https://doi.org/10.1109/ICITEE49829.2020.9271669
Aurup, G. M. (2011). User Preference Extraction from Bio-Signals : An Experimental Study Concordia University. Concordia University, Montreal, Quebec, Canada.
Batey, M. D. (2007). A psychometric investigation of everyday creativity. University of London.
Berridge, K. C., Robinson, T. E., &Aldridge, J. W. (2009). Dissecting components of reward : ’liking ’, “wanting”, and learning. Current Opinion in Pharmacology, 9(1), 65–73. https://doi.org/10.1016/j.coph.2008.12.014
Brachmann, A., &Redies, C. (2017). Computational and Experimental Approaches to Visual Aesthetics. 11(November), 1–17. https://doi.org/10.3389/fncom.2017.00102
Bradley, M. M. (2000). Emotion and Motivation. In Handbook of psychophysiology (pp. 602–642). Cambridge University Press.
Brown, S., Gao, X., Tisdelle, L., Eickhoff, S. B., &Liotti, M. (2011). Naturalizing aesthetics: Brain areas for aesthetic appraisal across sensory modalities. NeuroImage, 58(1). https://doi.org/10.1016/j.neuroimage.2011.06.012
Carson, S., Peterson, J., &Higgins, D. (2005). Reliability, Validity, and Factor Structure of the Creative Achievement Questionnaire. Creativity Research Journal, 17, 37–50. https://doi.org/10.1207/s15326934crj1701_4
Chatterjee, A., &Vartanian, O. (2014). Neuroaesthetics. Trends in Cognitive Sciences, 18(7). https://doi.org/10.1016/j.tics.2014.03.003
Chew, L. H., Teo, J., &Mountstephens1, J. (2016). Aesthetic preference recognition of 3D shapes using EEG. Cognitive Neurodynamics, 10(2), 165–173. https://doi.org/10.1007/s11571-015-9363-z
Cohen, J. (1977). Statistical Power Analysis for the Behavioral Sciences. https://doi.org/10.1016/c2013-0-10517-x
Cohen, M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice. https://doi.org/10.7551/mitpress/9609.001.0001
Davidson, R. J., &Irwin, W. (1999). The functional neuroanatomy of emotion and affective style. Trends in Cognitive Sciences, 3(1), 11–21. https://doi.org/10.1016/S1364-6613(98)01265-0
Dhar, R., &Gorlin, M. (2013). A dual-system framework to understand preference construction processes in choice. Journal of Consumer Psychology, 23(4), 528–542. https://doi.org/10.1016/j.jcps.2013.02.002
Dimberg, U. (1982). Facial Reactions to Facial Expressions. Psychophysiology, 19, 643–648. https://doi.org/10.1111/j.1469-8986.1982.tb02516.x
Dixon, W. J. (1960). Simplified Estimation from Censored Normal Samples. The Annals of Mathematical Statistics, 31(2), 385–391. https://doi.org/10.1214/aoms/1177705900
Dixon, W. J. (1980). Efficient analysis of experimental observations. Annual Review of Pharmacology and Toxicology, 20(1), 441–462. https://doi.org/10.1146/annurev.pa.20.040180.002301
Dunn, T. J., Baguley, T., &Brunsden, V. (2013). From alpha to omega : A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 1–14. https://doi.org/10.1111/bjop.12046
Ekman, P. (1999). Basic Emotions. In Hand book of cognition and emotion. John Wiley & Sons Ltd.
Ekman, P. (2003). Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. Times Books.
Era, V., Candidi, M., &Aglioti, S. M. (2015). Subliminal presentation of emotionally negative vs positive primes increases the perceived beauty of target stimuli. Experimental Brain Research, 233(11). https://doi.org/10.1007/s00221-015-4395-5
Eskine, K. J., Kacinik, N. A., &Prinz, J. J. (2012). Stirring images: Fear, not happiness or arousal, makes art more sublime. Emotion, 12(5). https://doi.org/10.1037/a0027200
Fingerhut, J., &Prinz, J. J. (2020). Aesthetic Emotions Reconsidered. The Monist, 103(2), 223–239. Retrieved from https://doi.org/10.1093/monist/onz037
Flexas, A., Rosselló, J., Christensen, J. F., Nadal, M., Olivera La Rosa, A., &Munar, E. (2013). Affective priming using facial expressions modulates liking for abstract art. PLoS ONE, 8(11). https://doi.org/10.1371/journal.pone.0080154
Freedberg, D., &Gallese, V. (2007). Motion , emotion and empathy in esthetic experience. TRENDS in Cognitive Sciences, 11(5). https://doi.org/10.1016/j.tics.2007.02.003
Gallese, V., &Sinigaglia, C. (2011). What is so special about embodied simulation ? Trends in Cognitive Sciences, 15(11), 512–519. https://doi.org/10.1016/j.tics.2011.09.003
Gartus, A., &Leder, H. (2013). The small step toward asymmetry : Aesthetic judgment of broken symmetries. Iperception, 4, 352–355.
Gatys, L. A., Ecker, A. S., &Bethge, M. (2016). Image Style Transfer Using Convolutional Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition, 2414–2423.
Gerger, G., Pelowski, M., &Ishizu, T. (2019). Does priming negative emotions really contribute to more positive aesthetic judgments? A comparative study of emotion priming paradigms using emotional faces versus emotional scenes and multiple negative emotions with fEMG. Emotion, 19(8). https://doi.org/10.1037/emo0000528
Gombrich, E. H. (2000). Art and illusion? a study in the psychology of pictorial representation. Princeton University Press.
Hadjidimitriou, S. K., Member, S., Hadjileontiadis, L. J., &Member, S. (2012). Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis. IEEE Transactions on Bio-Medical Engineering, 59. https://doi.org/10.1109/TBME.2012.2217495
Hakim, A., Klorfeld, S., Sela, T., Friedman, D., Shabat-Simon, M., &Levy, D. J. (2021). Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2020.10.005
Harmon-Jones, E., Harmon-Jones, C., &Summerell, E. (2017). On the importance of both dimensional and discrete models of emotion. Behavioral Sciences, 7(4). https://doi.org/10.3390/bs7040066
Howell, D. C. (2010). Statistical Methods for Psychology. Wadsworth.
Hsu, W., Lu, J., &Chien, C.-C. (2017). Emotion and Concentration Integrated System : Applied to the Detection and Analysis of Consumer Preference. 1512–1521. https://doi.org/10.24251/HICSS.2017.182
Jacobsen, T., Schubotz, R. I., Höfel, L., &Cramon, D. Y.V. (2006). Brain correlates of aesthetic judgment of beauty. NeuroImage, 29(1). https://doi.org/10.1016/j.neuroimage.2005.07.010
James, G., Witten, D., Hastie, T., &Tibshirani, R. (2013). Statistical Learning. In An Introduction to Statistical Learning：with Applications in R (pp. 26–28). Springer.
Johnson-frey, S. H., Maloof, F. R., Newman-norlund, R., Farrer, C., Inati, S., &Grafton, S. T. (2003). Actions or Hand-Object Interactions ? Human Inferior Frontal Cortex and Action Observation. Neuron, 39, 1053–1058.
Kawabata, H., &Zeki, S. (2004). Neural Correlates of Beauty. Journal of Neurophysiology, 91(4). https://doi.org/10.1152/jn.00696.2003
Khushaba, R. N., Greenacre, L., Al-Timemy, A., &Al-Jumaily, A. (2015). Event-related Potentials of Consumer Preferences. Procedia Computer Science, 68–73. https://doi.org/10.1016/j.procs.2015.12.277
Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., &Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems With Applications, 40(9), 3803–3812. https://doi.org/10.1016/j.eswa.2012.12.095
Kim, Y., Kang, K., Lee, H., &Bae, C. (2015). Preference Measurement Using User Response Electroencephalogram. Lecture Notes in Electrical Engineering, 1315–1324. https://doi.org/10.1007/978-3-662-45402-2
Lang, P. J., Bradley, M. M., &Cuthbert, B. N. (1997). International Affective Picture System (IAPS): Technical Manual and Affective Ratings.
Leder, H., Belke, B., Oeberst, A., &Augustin, D. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95(4), 489–508. https://doi.org/10.1348/0007126042369811
Leder, H., Gerger, G., Brieber, D., &Schwarz, N. (2014). What makes an art expert? Emotion and evaluation in art appreciation. Cognition and Emotion, 28(6), 1137–1147. https://doi.org/10.1080/02699931.2013.870132
Leder, H., Markey, P. S., &Pelowski, M. (2015). Aesthetic emotions to art – What they are and what makes them special Comment on “ The quartet theory of human emotions : An integrative and neurofunctional model ” by S . Koelsch et al . Physics of Life Reviews, 13, 67–70. https://doi.org/10.1016/j.plrev.2015.04.037
Leder, H., &Nadal, M. (2014). Ten years of a model of aesthetic appreciation and aesthetic judgments: The aesthetic episode - Developments and challenges in empirical aesthetics. British Journal of Psychology, 105(4), 443–464. https://doi.org/10.1111/bjop.12084
Luck, S. (2014). An Introduction to the Event-Related Potential Technique. Bradford Books.
McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates Publishers.
Morin, C. (2011). Neuromarketing : The New Science of Consumer Behavior. Society, 48, 131–135. https://doi.org/10.1007/s12115-010-9408-1
Mundloch, K., Winterberg, M., Hemmerich, W., Holzwig, P., Rupanova, A., Schönewolf, A., …Stevanov, J. (2017). Do happy faces really modulate liking for Jackson Pollock art and statistical fractal noise images? Psihologija, 50(3). https://doi.org/10.2298/PSI1703219M
Nicolas-alonso, L. F., &Gomez-gil, J. (2012). Brain Computer Interfaces, a Review. Sensors (Basel, Switzerland), 1211–1279. https://doi.org/10.3390/s120201211
Ortony, A., Clore, G. L., &Collins, A. (1988). The Cognitive Structure of Emotions. https://doi.org/10.1017/cbo9780511571299
Osgood, C. E., Suci, G. J., &Tannenbaum, P. H. (1957). The measurement of meaning. University of Illinois Press.
Ozkara, B. Y., &Bagozzi, R. (2021). The use of event related potentials brain methods in the study of Conscious and unconscious consumer decision making processes. Journal of Retailing and Consumer Services, 58, 102202. https://doi.org/10.1016/j.jretconser.2020.102202
Panksepp, J. (1982). Toward a general psychobiological theory of emotions. Behavioral and Brain Sciences, 5, 407–422. https://doi.org/10.1017/S0140525X00012759
Panksepp, J. (1998). Affective neuroscience: The foundations of human and animal emotions. Oxford University Press.
Panksepp, J. (2005). Affective Consciousness: Core Emotional Feelings in Animals and Humans. Consciousness and Cognition, 14, 30–80. https://doi.org/10.1016/j.concog.2004.10.004
Panksepp, J., &Watt, D. (2011). What is basic about basic emotions? Lasting lessons from affective neuroscience. Emotion Review, 3(4), 387–396. https://doi.org/10.1177/1754073911410741
Pfurtscheller, G., &Silva, F. H. (1999). Event-related EEG / MEG synchronization and desynchronization : basic principles. Clin Neurophysiol, 110, 1842–1857. https://doi.org/10.1016/s1388-2457(99)00141-8
Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R., …Taylor, M. J. (2000). Guidelines for using human event-related potentials to study cognition : Recording standards and publication criteria. Psychophysiology, 127–152.
Pissiota, A., Frans, Ö., Michelgård, Å., Appel, L., Långström, B., Flaten, M. A., &Fredrikson, M. (2003). Amygdala and anterior cingulate cortex activation during affective startle modulation: A PET study of fear. European Journal of Neuroscience, 18(5). https://doi.org/10.1046/j.1460-9568.2003.02855.x
Rhys, H. (2019). Machine Learning with R, tidyverse, and mlr. Manning Publications.
Schienle, A., Stark, R., Walter, B., Blecker, C., Ott, U., Kirsch, P., …Vaitl, D. (2002). The insula is not specifically involved in disgust processing: An fMRI study. NeuroReport, 13(16). https://doi.org/10.1097/00001756-200211150-00006
Smith, L. F., &Smith, J. K. (2006). The Nature and Growth of Aesthetic Fluency. In New directions in aesthetics, creativity and the arts. (pp. 47–58). https://doi.org/10.4324/9781315224084-5
Specker, E., Forster, M., Brinkmann, H., Boddy, J., Pelowski, M., Rosenberg, R., &Leder, H. (2018). The Vienna Art Interest and Art Knowledge Questionnaire ( VAIAK ): A Unified and Validated Measure of Art Interest and Art Knowledge. Psychology of Aesthetics Creativity and the Arts. https://doi.org/10.1037/aca0000205
Subbulakshmi, M. M., Balaganapathy, M., &Gerard, C. (2014). Wireless EEG Signals based Neuromarketing System using Fast Fourier Transform ( FFT ). 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, 25–30. https://doi.org/10.1109/CSPA.2014.6805714
Telpaz, A., Webb, R., &Levy, D. J. (2015). Using EEG to Predict Consumers’ Future Choices. Journal of Marketing Research. Retrieved from https://doi.org/10.1509/jmr.13.0564
Teo, J., Hou, C. L., &Mountstephens, J. (2018). Preference classification using Electroencephalography (EEG) and deep learning. Journal of Telecommunication, Electronic and Computer Engineering, 10(1–11), 87–91.
Thakral, P. P., Moo, L. R., &Slotnick, S. D. (2012). A neural mechanism for aesthetic experience. Neuroreport, 23, 310–313. https://doi.org/10.1097/WNR.0b013e328351759f
Touchette, B., &Lee, S. E. (2017). Measuring Neural Responses to Apparel Product Attractiveness: An Application of Frontal Asymmetry Theory. Clothing and Textiles Research Journal, 35(1), 3–15. https://doi.org/10.1177/0887302X16673157
Urgesi, C., Moro, V., Candidi, M., &Aglioti, S. M. (2006). Mapping Implied Body Actions in the Human Motor System. J Neurosci, 26(30), 7942–7949. https://doi.org/10.1523/JNEUROSCI.1289-06.2006
Vartanian, O., &Goel, V. (2004). Emotion Pathways in the Brain Mediate Aesthetic Preferences.pdf. Bulletin of Psychology and the Arts, 5, 37–42.
Vessel, E. A., &Biederman, I. (2007). The neural basis of scene preferences. Neuroreport, 18(6), 5–9. https://doi.org/10.1097/WNR.0b013e328091c1f9
Yadava, M., Kumar, P., Saini, R., Pratim, P., &Dogra, D. P. (2017). Analysis of EEG signals and its application to neuromarketing. Multimedia Tools and Applications, 19087–19111. https://doi.org/10.1007/s11042-017-4580-6
Yen, N. S., Chen, K. H., &Liu, E. H. (2010). Emotional modulation of the late positive potential (LPP) generalizes to Chinese individuals. International Journal of Psychophysiology, 75(3), 319–325. https://doi.org/10.1016/j.ijpsycho.2009.12.014