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  1. NTU Theses and Dissertations Repository
  2. 醫學院
  3. 腦與心智科學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78565
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor曾明宗(Ming-Tsung Tseng)
dc.contributor.authorPei-Yu Leeen
dc.contributor.author李珮羽zh_TW
dc.date.accessioned2021-07-11T15:04:20Z-
dc.date.available2029-12-31
dc.date.copyright2019-08-28
dc.date.issued2019
dc.date.submitted2019-08-15
dc.identifier.citationBartra O, McGuire JT, Kable JW (2013) The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage.
Clithero JA, Rangel A (2013) Informatic parcellation of the network involved in the computation of subjective value. Soc Cogn Affect Neurosci.
Delgado MR, Labouliere CD, Phelps EA (2006) Fear of losing money? Aversive conditioning with secondary reinforcers. Soc Cogn Affect Neurosci.
Garrison J, Erdeniz B, Done J (2013) Prediction error in reinforcement learning: A meta-analysis of neuroimaging studies. Neurosci Biobehav Rev.
Jensen J, Smith AJ, Willeit M, Crawley AP, Mikulis DJ, Vitcu I, Kapur S (2007) Separate brain regions code for salience vs. valence during reward prediction in humans. Hum Brain Mapp 28:294–302.
Kahnt T, Park SQ, Cohen MX, Beck A, Heinz A, Wrase J (2009) Dorsal striatal-midbrain connectivity in humans predicts how reinforcements are used to guide decisions. J Cogn Neurosci.
Kim H, Shimojo S, O’Doherty JP (2006) Is avoiding an aversive outcome rewarding? Neural substrates of avoidance learning in the human brain. PLoS Biol 4:1453–1461.
Knutson B, Katovich K, Suri G (2014) Inferring affect from fMRI data. Trends Cogn Sci.
Lawson RP, Seymour B, Loh E, Lutti A, Dolan RJ, Dayan P, Weiskopf N, Roiser JP (2014) The habenula encodes negative motivational value associated with primary punishment in humans. Proc Natl Acad Sci 111:11858–11863.
Liu X, Hairston J, Schrier M, Fan J (2011) Common and distinct networks underlying reward valence and processing stages: A meta-analysis of functional neuroimaging studies. Neurosci Biobehav Rev.
Olsson A, Phelps EA (2007) Social learning of fear. Nat Neurosci.
Palminteri S, Justo D, Jauffret C, Pavlicek B, Dauta A, Delmaire C, Czernecki V, Karachi C, Capelle L, Durr A, Pessiglione M (2012) Critical Roles for Anterior Insula and Dorsal Striatum in Punishment-Based Avoidance Learning. Neuron.
Palminteri S, Khamassi M, Joffily M, Coricelli G (2015) Contextual modulation of value signals in reward and punishment learning. Nat Commun 6:1–14 Available at: http://dx.doi.org/10.1038/ncomms9096.
Pessiglione M, Seymour B, Flandin G, Dolan RJ, Frith CD (2006) Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature.
Schultz W, Dayan P, Montague PR (1997) A Neural Substrate of Prediction and Reward on JSTOR. 275:1593–1599 Available at: http://www.jstor.org/stable/2893707?seq=1#page_scan_tab_contents.
Sescousse G, Caldú X, Segura B, Dreher JC (2013) Processing of primary and secondary rewards: A quantitative meta-analysis and review of human functional neuroimaging studies. Neurosci Biobehav Rev.
Seymour B, Daw N, Dayan P, Singer T, Dolan R (2007) Differential Encoding of Losses and Gains in the Human Striatum. J Neurosci 27:4826–4831 Available at: http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.0400-07.2007.
Seymour B, O’Doherty JP, Koltzenburg M, Wiech K, Frackowiak R, Friston K, Dolan R (2005) Opponent appetitive-aversive neural processes underlie predictive learning of pain relief. Nat Neurosci 8:1234–1240.
Skvortsova V, Palminteri S, Pessiglione M (2014) Learning To Minimize Efforts versus Maximizing Rewards: Computational Principles and Neural Correlates. J Neurosci 34:15621–15630.
Wagner AR, Rescorla RA (1972) Inhibition in Pavlovian Conditioning: application of a theory. Inhib Learn.
Yacubian J (2006) Dissociable Systems for Gain- and Loss-Related Value Predictions and Errors of Prediction in the Human Brain. J Neurosci 26:9530–9537 Available at: http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.2915-06.2006.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78565-
dc.description.abstract增強式學習的獎勵和懲罰對於人類的行為是很重要的調節因子,然而獎勵學習與懲罰學習是否由相同神經機制反映仍有許多爭論。此外,人類經常在同一個時間點學習如何最大化獎勵與最小化懲罰,但是迄今僅有極少數的研究在探討獎勵學習與懲罰學習的交互作用以及初級增強物與次級增強物的差異。我們利用功能性磁振造影於正常成人進行增強式學習任務時檢測其腦部反應,欲探討獎勵學習與懲罰學習之交互作用,並觀察初級增強物和次級增強物有無差異。目前研究支持獎勵學習與懲罰學習為不同的神經機制,此外,在交互作用下僅有懲罰學習會影響獎勵學習,且初級增強物和次級增強物似乎並無明顯差異。這些結果表示當獎勵學習與懲罰學習具有競爭性關係,懲罰學習會較獎勵學習優勢。此研究中可以增強我們對於增強式學習的神經科學知識。zh_TW
dc.description.abstractReward and punishment are important modulators of reinforcement learning to guide human behaviors. However, it remains elusive whether reward learning and punishment learning would share the same neural mechanism. Although humans usually learn to maximize reward and minimize punishment at the same time, the mutual influence between reward and punishment learning and the difference between primary and secondary reinforcers remain largely unknown. The current research investigated the interaction between reward learning and punishment learning as well as the differences between primary and secondary punishment, by using functional magnetic resonance imaging in combination with probabilistic instrumental learning tasks and genetic screening in healthy adults. Our findings support the view that reward learning and punishment learning may have different mechanisms. In addition, we found reward learning was interfered by punishment learning but not vice versa, and it seemed that there was no significant difference between primary and secondary punishment. These findings suggest that when reward learning conflicted with punishment learning, punishment learning would be more dominant. Results obtained from these investigations can enhance our understanding about reinforcement learning.en
dc.description.provenanceMade available in DSpace on 2021-07-11T15:04:20Z (GMT). No. of bitstreams: 1
ntu-108-R06454007-1.pdf: 894073 bytes, checksum: 0ab843a570ca1b55d1dd78954d692507 (MD5)
Previous issue date: 2019
en
dc.description.tableofcontents中文摘要 i
ABSTRACT ii
CONTENTS iii
INTRODUCTION 1
METHODS 3
Participants 3
Stimuli 3
Electrocutaneous stimulus 3
Visual stimulus 4
Calibration phase 4
Practice phase 4
Titration phase 5
Experimental paradigm 5
Post-scanning phase 7
Statistical analysis 7
fMRI data acquisition 7
Computational model 8
fMRI data analysis 9
RESULTS 10
Validation 10
Behavioral results 11
Imaging results 12
DISCUSSION 14
Different neural networks for RL vs PL 14
Interference between RL and PL 15
CONCLUSION 17
REFERENCES 18
LIST OF FIGURES AND TABLES 21
dc.language.isoen
dc.subject獎勵zh_TW
dc.subject機器學習zh_TW
dc.subject次級增強物zh_TW
dc.subject懲罰zh_TW
dc.subject初級增強物zh_TW
dc.subject增強式學習zh_TW
dc.subjectmachine learningen
dc.subjectreinforcementen
dc.subjectrewarden
dc.subjectpunishmenten
dc.subjectprimary reinforceren
dc.subjectsecondary reinforceren
dc.title獎賞性學習與懲罰性學習交互作用之神經基礎zh_TW
dc.titleThe Neural Mechanism Underlying the Interaction between Reward and Punishment Learningen
dc.typeThesis
dc.date.schoolyear107-2
dc.description.degree碩士
dc.contributor.oralexamcommittee吳恩賜(Joshua O. Goh),林士傑(Shih-Chieh Lin)
dc.subject.keyword增強式學習,獎勵,懲罰,初級增強物,次級增強物,機器學習,zh_TW
dc.subject.keywordreinforcement,reward,punishment,primary reinforcer,secondary reinforcer,machine learning,en
dc.relation.page32
dc.identifier.doi10.6342/NTU201903814
dc.rights.note有償授權
dc.date.accepted2019-08-16
dc.contributor.author-college醫學院zh_TW
dc.contributor.author-dept腦與心智科學研究所zh_TW
dc.date.embargo-lift2029-12-31-
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