| dc.contributor.advisor | López López, Juan Manuel |
| dc.contributor.advisor | León Anhuaman, Laura Andrea |
| dc.creator | Sastoque Granados, Santiago |
| dc.date.accessioned | 2021-06-16T20:16:55Z |
| dc.date.available | 2021-06-16T20:16:55Z |
| dc.date.created | 2021-05-27 |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/31624 |
| dc.description | El aprendizaje por refuerzo clásico (CRL, por sus siglas en inglés), ha sido utilizado ampliamente en aplicaciones para la psicología y neurociencia. Sin embargo, el aprendizaje por refuerzo cuántico (QRL, por sus siglas en inglés) ha demostrado mejor desempeño en simulaciones por computadora. Para poder analizar la toma de decisiones basada en el valor utilizando estos modelos, se diseñó un protocolo experimental que consiste en dos grupos sanos de diferentes edades realizando la prueba Iowa Gambling Task. Con esta base de datos se comparó el desempeño de cuatro modelos de CRL y uno de QRL, los resultados demostraron que la toma de decisiones basadas en el valor se puede modelar utilizando aprendizaje por refuerzo cuántico y esto sugiere que el enfoque cuántico a la toma de decisiones aporta nuevas perspectivas y herramientas que permiten entender nuevos aspectos del proceso de toma de decisiones humano. |
| dc.description.abstract | Classical reinforcement learning (CRL) has been widely used in psychology and neuroscience applications. However, quantum reinforcement learning (QRL) has shown better performance in computer simulations. In order to analyze value-based decision-making using these models, an experimental protocol was designed, consisting of two healthy groups of different ages performing the Iowa Gambling Task. The results showed that value-based decision making can be modeled using quantum reinforcement learning and this suggests that the quantum approach to decision making provides new perspectives and tools that allow to understand new aspects of the human decision making process. |
| dc.format.extent | 50 pp. |
| dc.format.mimetype | application/pdf |
| dc.language.iso | spa |
| dc.subject | Aprendizaje por refuerzo cuántico |
| dc.subject | Toma de decisiones |
| dc.subject | Aprendizaje por refuerzo |
| dc.subject.ddc | Ingeniería & operaciones afines |
| dc.subject.lemb | Diseño en ingeniería |
| dc.title | Modelo de toma de decisiones utilizando aprendizaje por refuerzo cuántico |
| dc.type | bachelorThesis |
| dc.publisher | Universidad del Rosario |
| dc.creator.degree | Ingeniero Biomédico |
| dc.publisher.program | Ingeniería Biomédica |
| dc.publisher.department | Escuela de Medicina y Ciencias de la Salud |
| dc.subject.keyword | Quantum reinforcement learning |
| dc.subject.keyword | Value-based decision-making |
| dc.subject.keyword | Iowa Gambling |
| dc.subject.keyword | Task |
| dc.subject.keyword | Reinforcement learning |
| dc.rights.accesRights | info:eu-repo/semantics/openAccess |
| dc.type.spa | Trabajo de grado |
| dc.rights.acceso | Abierto (Texto Completo) |
| dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion |
| dc.source.bibliographicCitation | Sutton, Richard S; Barto, Andrew G (2018) Reinforcement learning: An introduction. : MIT press; 0262352702; |
| dc.source.bibliographicCitation | Sutton, Richard S (1988) Learning to predict by the methods of temporal differences. En:Machine learning; Vol. 3; No. 1; pp. 9 - 44; Springer; |
| dc.source.bibliographicCitation | Grover, Lov K (1997) Quantum mechanics helps in searching for a needle in a haystack. En:Physical review letters; Vol. 79; No. 2; pp. 325 - 325; APS; |
| dc.source.bibliographicCitation | Chen, Chun-Lin; Dong, Dao-Yi (2008) Superposition-inspired reinforcement learning and quantum reinforcement learning. En:Reinforcement Learning; IntechOpen; |
| dc.source.bibliographicCitation | Shankar, Ramamurti (2012) Principles of quantum mechanics. : Springer Science & Business Media; 147570576X; |
| dc.source.bibliographicCitation | Kaelbling, Leslie Pack; Littman, Michael L; Moore, Andrew W (1996) Reinforcement learning: A survey. En:Journal of artificial intelligence research; Vol. 4; pp. 237 - 285; |
| dc.source.bibliographicCitation | Erev, Ido; Barron, Greg (2005) On adaptation, maximization, and reinforcement learning among cognitive strategies. En:Psychological review; Vol. 112; No. 4; pp. 912 - 912; American Psychological Association; |
| dc.source.bibliographicCitation | Ekhtiari, Hamed; Paulus, Martin (2016) Neuroscience for Addiction Medicine: From Prevention to Rehabilitation-Methods and Interventions. : Elsevier; 0444637400; |
| dc.source.bibliographicCitation | Redgrave, Peter; Prescott, Tony J; Gurney, Kevin (1999) The basal ganglia: a vertebrate solution to the selection problem?. En:Neuroscience; Vol. 89; No. 4; pp. 1009 - 1023; Elsevier; |
| dc.source.bibliographicCitation | Stoet, Gijsbert (2016) PsyToolkit: A Novel Web-Based Method for Running Online Questionnaires and Reaction-Time Experiments. En:Teaching of Psychology; Vol. 44; No. 1; pp. 24 - 31; SAGE Publications Inc; Disponible en: https://doi.org/10.1177/0098628316677643. Disponible en: 10.1177/0098628316677643. |
| dc.source.bibliographicCitation | Kondo, Toshiyuki; Ito, Koji (2004) A reinforcement learning with evolutionary state recruitment strategy for autonomous mobile robots control. En:Robotics and Autonomous Systems; Vol. 46; No. 2; pp. 111 - 124; Disponible en: https://www.sciencedirect.com/science/article/pii/S0921889003001842. Disponible en: https://doi.org/10.1016/j.robot.2003.11.006. |
| dc.source.bibliographicCitation | Spear, L P (2000) The adolescent brain and age-related behavioral manifestations. En:Neuroscience & Biobehavioral Reviews; Vol. 24; No. 4; pp. 417 - 463; Disponible en: https://www.sciencedirect.com/science/article/pii/S0149763400000142. Disponible en: https://doi.org/10.1016/S0149-7634(00)00014-2. |
| dc.source.bibliographicCitation | Xue, Gui; Lu, Zhonglin; Levin, Irwin P; Bechara, Antoine (2010) The impact of prior risk experiences on subsequent risky decision-making: The role of the insula. En:NeuroImage; Vol. 50; No. 2; pp. 709 - 716; Disponible en: https://www.sciencedirect.com/science/article/pii/S105381190901386X. Disponible en: https://doi.org/10.1016/j.neuroimage.2009.12.097. |
| dc.source.bibliographicCitation | Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu; Basieva, Irina; Khrennikov, Andrei (2011) Quantum-like model of brain's functioning: Decision making from decoherence. En:Journal of Theoretical Biology; Vol. 281; No. 1; pp. 56 - 64; Disponible en: https://www.sciencedirect.com/science/article/pii/S0022519311002220. Disponible en: https://doi.org/10.1016/j.jtbi.2011.04.022. |
| dc.source.bibliographicCitation | Niv, Yael (2009) Reinforcement learning in the brain. En:Journal of Mathematical Psychology; Vol. 53; No. 3; pp. 139 - 154; Disponible en: https://www.sciencedirect.com/science/article/pii/S0022249608001181. Disponible en: https://doi.org/10.1016/j.jmp.2008.12.005. |
| dc.source.bibliographicCitation | Schachter, Stanley; Singer, Jerome (1962) Cognitive, social, and physiological determinants of emotional state. En:Psychological Review; Vol. 69; No. 5; pp. 379 - 399; US: American Psychological Association; Disponible en: 10.1037/h0046234. |
| dc.source.bibliographicCitation | Busemeyer, Jerome R; Stout, Julie C (2002) A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. En:Psychological Assessment; Vol. 14; No. 3; pp. 253 - 262; Busemeyer, Jerome R.: Indiana U, Dept of Psychology, Bloomington, IN, US, 47405, jbusemey@indiana.edu: American Psychological Association; 1939-134X(Electronic),1040-3590(Print); Disponible en: 10.1037/1040-3590.14.3.253. |
| dc.source.bibliographicCitation | (2004) Blackwell handbook of judgment and decision making. En:Blackwell handbook of judgment and decision making.; pp. xvi, 664 - xvi, 664; Malden: Blackwell Publishing; 1-4051-0746-4 (Hardcover); Disponible en: 10.1002/9780470752937. |
| dc.source.bibliographicCitation | Kawagoe, Reiko; Takikawa, Yoriko; Hikosaka, Okihide (2004) Reward-Predicting Activity of Dopamine and Caudate Neurons—A Possible Mechanism of Motivational Control of Saccadic Eye Movement. En:Journal of Neurophysiology; Vol. 91; No. 2; pp. 1013 - 1024; American Physiological Society; Disponible en: https://doi.org/10.1152/jn.00721.2003. Disponible en: 10.1152/jn.00721.2003. |
| dc.source.bibliographicCitation | Haines, Nathaniel; Vassileva, Jasmin; Ahn, Woo-Young (2018) The Outcome-Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task. En:Cognitive Science; Vol. 42; No. 8; pp. 2534 - 2561; John Wiley & Sons, Ltd; Disponible en: https://doi.org/10.1111/cogs.12688. Disponible en: https://doi.org/10.1111/cogs.12688. |
| dc.source.bibliographicCitation | Ahn, Woo-Young; Busemeyer, Jerome R; Wagenmakers, Eric-Jan; Stout, Julie C (2008) Comparison of Decision Learning Models Using the Generalization Criterion Method. En:Cognitive Science; Vol. 32; No. 8; pp. 1376 - 1402; John Wiley & Sons, Ltd; Disponible en: https://doi.org/10.1080/03640210802352992. Disponible en: https://doi.org/10.1080/03640210802352992. |
| dc.source.bibliographicCitation | Dong, D; Chen, C; Li, H; Tarn, T (2008) Quantum Reinforcement Learning. En:IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics); Vol. 38; No. 5; pp. 1207 - 1220; Disponible en: 10.1109/TSMCB.2008.925743. |
| dc.source.bibliographicCitation | Ahn, Woo-Young; Haines, Nathaniel; Zhang, Lei (2017) Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package. En:Computational Psychiatry; Vol. 1; pp. 24 - 57; Disponible en: https://doi.org/10.1162/CPSY_a_00002. Disponible en: 10.1162/CPSY_a_00002. |
| dc.source.bibliographicCitation | Doya, Kenji (2000) Reinforcement Learning in Continuous Time and Space. En:Neural Computation; Vol. 12; No. 1; pp. 219 - 245; Disponible en: https://doi.org/10.1162/089976600300015961. Disponible en: 10.1162/089976600300015961. |
| dc.source.bibliographicCitation | Rescorla, R; Wagner, Allan (1972) A theory of Pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement. En:Classical Conditioning: Current Research and Theory; |
| dc.source.bibliographicCitation | Preskill, John (1998) Reliable quantum computers. En:Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences; Vol. 454; No. 1969; pp. 385 - 410; Royal Society; Disponible en: https://doi.org/10.1098/rspa.1998.0167. Disponible en: 10.1098/rspa.1998.0167. |
| dc.source.bibliographicCitation | Yukalov, V I; Sornette, D (2016) Quantum probability and quantum decision-making. En:Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences; Vol. 374; No. 2058; pp. 20150100 - 20150100; Royal Society; Disponible en: https://doi.org/10.1098/rsta.2015.0100. Disponible en: 10.1098/rsta.2015.0100. |
| dc.source.bibliographicCitation | Busemeyer, Jerome R; Bruza, Peter D (2012) Quantum Models of Cognition and Decision. Cambridge: Cambridge University Press; 9781107011991; Disponible en: https://www.cambridge.org/core/books/quantum-models-of-cognition-and-decision/75909428F710F7C6AF7D580CB83443AC. Disponible en: DOI: 10.1017/CBO9780511997716. |
| dc.source.bibliographicCitation | Payne, John W; Bettman, James R; Johnson, Eric J (1992) Behavioral Decision Research: A Constructive Processing Perspective. En:Annual Review of Psychology; Vol. 43; No. 1; pp. 87 - 131; Annual Reviews; Disponible en: https://doi.org/10.1146/annurev.ps.43.020192.000511. Disponible en: 10.1146/annurev.ps.43.020192.000511. |
| dc.source.bibliographicCitation | Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan (2012) Neural Basis of Reinforcement Learning and Decision Making. En:Annual Review of Neuroscience; Vol. 35; No. 1; pp. 287 - 308; Annual Reviews; Disponible en: https://doi.org/10.1146/annurev-neuro-062111-150512. Disponible en: 10.1146/annurev-neuro-062111-150512. |
| dc.source.bibliographicCitation | Gold, Joshua I; Shadlen, Michael N (2007) The Neural Basis of Decision Making. En:Annual Review of Neuroscience; Vol. 30; No. 1; pp. 535 - 574; Annual Reviews; Disponible en: https://doi.org/10.1146/annurev.neuro.29.051605.113038. Disponible en: 10.1146/annurev.neuro.29.051605.113038. |
| dc.source.bibliographicCitation | Khrennikov, Andrei; Asano, Masanari (2020) A Quantum-Like Model of Information Processing in the Brain. En:Applied Sciences; Vol. 10; No. 2; 2076-3417; Disponible en: 10.3390/app10020707. |
| dc.source.bibliographicCitation | Bechara, Antoine; Damasio, Antonio R; Damasio, Hanna; Anderson, Steven W (1994) Insensitivity to future consequences following damage to human prefrontal cortex. En:Cognition; Vol. 50; No. 1; pp. 7 - 15; Disponible en: https://www.sciencedirect.com/science/article/pii/0010027794900183. Disponible en: https://doi.org/10.1016/0010-0277(94)90018-3. |
| dc.source.bibliographicCitation | Byrne, Kaileigh A; Norris, Dominique D; Worthy, Darrell A (2016) Dopamine, depressive symptoms, and decision-making: the relationship between spontaneous eye blink rate and depressive symptoms predicts Iowa Gambling Task performance. En:Cognitive, Affective, & Behavioral Neuroscience; Vol. 16; No. 1; pp. 23 - 36; Disponible en: https://doi.org/10.3758/s13415-015-0377-0. Disponible en: 10.3758/s13415-015-0377-0. |
| dc.source.bibliographicCitation | Stoet, Gijsbert (2010) PsyToolkit: A software package for programming psychological experiments using Linux. En:Behavior Research Methods; Vol. 42; No. 4; pp. 1096 - 1104; Disponible en: https://doi.org/10.3758/BRM.42.4.1096. Disponible en: 10.3758/BRM.42.4.1096. |
| dc.source.bibliographicCitation | Yechiam, Eldad; Busemeyer, Jerome R (2005) Comparison of basic assumptions embedded in learning models for experience-based decision making. En:Psychonomic Bulletin & Review; Vol. 12; No. 3; pp. 387 - 402; Disponible en: https://doi.org/10.3758/BF03193783. Disponible en: 10.3758/BF03193783. |
| dc.source.bibliographicCitation | Ahn, Woo-Young; Vasilev, Georgi; Lee, Sung-Ha; Busemeyer, Jerome R; Kruschke, John K; Bechara, Antoine; Vassileva, Jasmin (2014) Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. En:Frontiers in Psychology; Vol. 5; pp. 849 - 849; 1664-1078; Disponible en: https://www.frontiersin.org/article/10.3389/fpsyg.2014.00849. |
| dc.source.bibliographicCitation | Worthy, Darrell; Pang, Bo; Byrne, Kaileigh (2013) Decomposing the roles of perseveration and expected value representation in models of the Iowa gambling task. En:Frontiers in Psychology; Vol. 4; pp. 640 - 640; 1664-1078; Disponible en: https://www.frontiersin.org/article/10.3389/fpsyg.2013.00640. |
| dc.source.bibliographicCitation | Worthy, Darrell; Maddox, W Todd (2012) Age-Based Differences in Strategy Use in Choice Tasks. En:Frontiers in Neuroscience; Vol. 5; pp. 145 - 145; 1662-453X; Disponible en: https://www.frontiersin.org/article/10.3389/fnins.2011.00145. |
| dc.source.bibliographicCitation | Erev, Ido; Roth, Alvin E (1998) Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria. En:The American Economic Review; Vol. 88; No. 4; pp. 848 - 881; American Economic Association; Disponible en: http://www.jstor.org/stable/117009. |
| dc.source.bibliographicCitation | Li, Ji-An; Dong, Daoyi; Wei, Zhengde; Liu, Ying; Pan, Yu; Nori, Franco; Zhang, Xiaochu (2020) Quantum reinforcement learning during human decision-making. En:Nature Human Behaviour; Vol. 4; No. 3; pp. 294 - 307; Disponible en: https://doi.org/10.1038/s41562-019-0804-2. Disponible en: 10.1038/s41562-019-0804-2. |
| dc.source.bibliographicCitation | Rangel, Antonio; Camerer, Colin; Montague, P Read (2008) A framework for studying the neurobiology of value-based decision making. En:Nature Reviews Neuroscience; Vol. 9; No. 7; pp. 545 - 556; Disponible en: https://doi.org/10.1038/nrn2357. Disponible en: 10.1038/nrn2357. |
| dc.source.bibliographicCitation | Kringelbach, Morten L (2005) The human orbitofrontal cortex: linking reward to hedonic experience. En:Nature Reviews Neuroscience; Vol. 6; No. 9; pp. 691 - 702; Disponible en: https://doi.org/10.1038/nrn1747. Disponible en: 10.1038/nrn1747. |
| dc.source.bibliographicCitation | Silver, David; Huang, Aja; Maddison, Chris J; Guez, Arthur; Sifre, Laurent; van den Driessche, George; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis (2016) Mastering the game of Go with deep neural networks and tree search. En:Nature; Vol. 529; No. 7587; pp. 484 - 489; Disponible en: https://doi.org/10.1038/nature16961. Disponible en: 10.1038/nature16961. |
| dc.source.bibliographicCitation | Duell, Natasha; Steinberg, Laurence; Icenogle, Grace; Chein, Jason; Chaudhary, Nandita; Di Giunta, Laura; Dodge, Kenneth A; Fanti, Kostas A; Lansford, Jennifer E; Oburu, Paul; Pastorelli, Concetta; Skinner, Ann T; Sorbring, Emma; Tapanya, Sombat; Uribe Tirado, Liliana Maria; Alampay, Liane Peña; Al-Hassan, Suha M; Takash, Hanan M S; Bacchini, Dario; Chang, Lei (2018) Age Patterns in Risk Taking Across the World. En:Journal of Youth and Adolescence; Vol. 47; No. 5; pp. 1052 - 1072; Disponible en: https://doi.org/10.1007/s10964-017-0752-y. Disponible en: 10.1007/s10964-017-0752-y. |
| dc.source.bibliographicCitation | Tversky, Amos; Kahneman, Daniel (1992) Advances in prospect theory: Cumulative representation of uncertainty. En:Journal of Risk and Uncertainty; Vol. 5; No. 4; pp. 297 - 323; Disponible en: https://doi.org/10.1007/BF00122574. Disponible en: 10.1007/BF00122574. |
| dc.rights.licencia | EL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma. |
| dc.contributor.gruplac | GiBiome |
| dc.type.document | Trabajo de grado |
| dc.identifier.doi | https://doi.org/10.48713/10336_31624 |
| dc.creator.degreetype | Full time |
| dc.title.TranslatedTitle | Decision-making model using quantum reinforcement learning |
| dc.source.instname | instname:Universidad del Rosario |
| dc.source.instname | instname:Universidad del Rosario |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR |
