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A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model Cover

A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model

Open Access
|Nov 2020

References

  1. Ahn, W.-Y. , Haines, N. , & Zhang, L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1, 2457. DOI: https://doi.org/10.1162/CPSY_a_00002, PMID: 29601060, PMCID: PMC5869013
  2. Ahn, W.-Y. , Krawitz, A. , Kim, W. , Busemeyer, J. R. , & Brown, J. W. (2013). A model-based fMRI analysis with hierarchical Bayesian parameter estimation. Decision, 1(S), 823. DOI: https://doi.org/10.1037/2325-9965.1.S.8
  3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.
  4. Camerer, C. , & Ho, T.-H. (1999). Experience-weighted attraction learning in normal form games. Econometrica, 67(4), 827874. DOI: https://doi.org/10.1111/1468-0262.00054
  5. Camerer, C. F. (2003). Behavioral game theory: Experiments in strategic interaction. Princeton, NJ: Princeton University Press.
  6. Camerer, C. F. , Ho, T.-H. , & Chong, J.-K. (2002). Sophisticated experience-weighted attraction learning and strategic teaching in repeated games. Journal of Economic Theory, 104(1), 137188. DOI: https://doi.org/10.1006/jeth.2002.2927
  7. Canini, K. R. , Shashkov, M. M. , & Griffiths, T. L. (2010). Modeling transfer learning in human categorization with the hierarchical Dirichlet process. Retrieved from https://icml.cc/Conferences/2010/papers/180.pdf
  8. Cheung, Y.-W. , & Friedman, D. (1997). Individual learning in normal form games: Some laboratory results. Games and Economic Behavior, 19(1), 4676. DOI: https://doi.org/10.1006/game.1997.0544
  9. Chiu, P. H. , Kayali, M. A. , Kishida, K. T. , Tomlin, D. , Klinger, L. G. , Klinger, M. R. , & Montague, P. R. (2008). Self responses along cingulate cortex reveal quantitative neural phenotype for high- functioning autism. Neuron, 57(3), 463473. DOI: https://doi.org/10.1016/j.neuron.2007.12.020, PMID: 18255038, PMCID: PMC4512741
  10. Collins, A. G. (2018). The tortoise and the hare: Interactions between reinforcement learning and working memory. Journal of Cognitive Neuroscience, 30(10), 14221432. DOI: https://doi.org/10.1162/jocn_a_01238, PMID: 29346018
  11. Cools, R. , Clark, L. , Owen, A. M. , & Robbins, T. W. (2002). Defining the neural mechanisms of probabilistic reversal learning using event-related functional magnetic resonance imaging. Journal of Neuroscience, 22(11), 45634567. DOI: https://doi.org/10.1523/JNEUROSCI.22-11-04563.2002, PMID: 12040063, PMCID: PMC6758810
  12. Crawley, D. , Zhang, L. , Jones, E. J. , Ahmad, J. , Caceres, A. S. J. , Oakley, B. , … Loth, E. (2019). Modeling cognitive flexibility in autism spectrum disorder and typical development reveals comparable developmental shifts in learning mechanisms. PsyArXiv. DOI: https://doi.org/10.31234/osf.io/h7jcm
  13. Daw, N. D. (2011). Trial-by-trial data analysis using computational models. In M. R. Delgado , E. A. Phelps , and T. W. Robbins (Eds.), Decision making, affect, and learning: Attention and performance XXIII. Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780199600434.003.0001
  14. den Ouden, H. E. , Daw, N. D. , Fernandez, G. , Elshout, J. A. , Rijpkema, M. , Hoogman, M. , … Cools, R. (2013). Dissociable effects of dopamine and serotonin on reversal learning. Neuron, 80(4), 10901100. DOI: https://doi.org/10.1016/j.neuron.2013.08.030, PMID: 24267657
  15. Doll, B. B. , Bath, K. G. , Daw, N. D. , & Frank, M. J. (2016). Variability in dopamine genes dissociates model-based and model-free reinforcement learning. Journal of Neuroscience, 36(4), 12111222. DOI: https://doi.org/10.1523/JNEUROSCI.1901-15.2016, PMID: 26818509, PMCID: PMC4728725
  16. Friston, K. J. , Stephan, K. E. , Montague, R. , & Dolan, R. J. (2014). Computational psychiatry: The brain as a phantastic organ. The Lancet Psychiatry, 1(2), 148158. DOI: https://doi.org/10.1016/S2215-0366(14)70275-5
  17. Galla, T. , & Farmer, J. D. (2013). Complex dynamics in learning complicated games. Proceedings of the National Academy of Sciences, 110(4), 12321236. DOI: https://doi.org/10.1073/pnas.1109672110, PMID: 23297213, PMCID: PMC3557065
  18. Gelman, A. , Carlin, J. B. , Stern, H. S. , Dunson, D. B. , Vehtari, A. , & Rubin, D. B. (2013). Bayesian data analysis. Boca Raton, FL: Chapman and Hall/CRC. DOI: https://doi.org/10.1201/b16018
  19. Geweke, J. (1991). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments (Vol. 196). Mineapolis, MN: Federal Reserve Bank of Minneapolis, Research Department. DOI: https://doi.org/10.21034/sr.148
  20. Hampton, A. N. , Bossaerts, P. , & O’Doherty, J. P. (2008). Neural correlates of mentalizing-related computations during strategic interactions in humans. Proceedings of the National Academy of Sciences, 105(18), 67416746. DOI: https://doi.org/10.1073/pnas.0711099105, PMID: 18427116, PMCID: PMC2373314
  21. Ho, T. H. , Camerer, C. F. , & Chong, J.-K. (2007). Self-tuning experience weighted attraction learning in games. Journal of Economic Theory, 133(1), 177198. DOI: https://doi.org/10.1016/j.jet.2005.12.008
  22. Ho, T. H. , Wang, X. , & Camerer, C. F. (2007). Individual differences in EWA learning with partial payoff information. The Economic Journal, 118(525), 3759. DOI: https://doi.org/10.1111/j.1468-0297.2007.02103.x
  23. Hunter, L. E. , Meer, E. A. , Gillan, C. M. , Hsu, M. , & Daw, N. D. (2019). Excessive deliberation in social anxiety. bioRxiv, 522433. DOI: https://doi.org/10.1101/522433
  24. Huys, Q. J. , Lally, N. , Faulkner, P. , Eshel, N. , Seifritz, E. , Gershman, S. J. , Roiser, J. P. (2015). Interplay of approximate planning strategies. Proceedings of the National Academy of Sciences, 112(10), 30983103.
  25. King-Casas, B. , Sharp, C. , Lomax-Bream, L. , Lohrenz, T. , Fonagy, P. , & Montague, P. R. (2008). The rupture and repair of cooperation in borderline personality disorder. Science, 321(5890), 806810. DOI: https://doi.org/10.1126/science.1156902, PMID: 18687957, PMCID: PMC4105006
  26. Lee, D. (2013). Decision making: From neuroscience to psychiatry. Neuron, 78(2), 233248. DOI: https://doi.org/10.1016/j.neuron.2013.04.008, PMID: 23622061, PMCID: PMC3670825
  27. Lee, D. , Seo, H. , & Jung, M. W. (2012). Neural basis of reinforcement learning and decision making. Annual Review of Neuroscience, 35, 287308. DOI: https://doi.org/10.1146/annurev-neuro-062111-150512, PMID: 22462543, PMCID: PMC3490621
  28. Ly, A. , Boehm, U. , Heathcote, A. , Turner, B. M. , Forstmann, B. , Marsman, M. , & Matzke, D. (2017). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In A. A. Moustafa (Ed.), Computational models of brain and behavior (pp. 467480). Hoboken, NJ: John Wiley. DOI: https://doi.org/10.1002/9781119159193.ch34
  29. Moutoussis, M. , Hopkins, A. K. , & Dolan, R. J. (2018). Hypotheses about the relationship of cognition with psychopathology should be tested by embedding them into empirical priors. Frontiers in Psychology, 9, 2504. DOI: https://doi.org/10.3389/fpsyg.2018.02504
  30. O’Doherty, J. P. , Hampton, A. , & Kim, H. (2007). Model-based fMRI and its application to reward learning and decision making. Annals of the New York Academy of Sciences, 1104(1), 3553. DOI: https://doi.org/10.1196/annals.1390.022, PMID: 17416921
  31. Piray, P. , Dezfouli, A. , Heskes, T. , Frank, M. J. , & Daw, N. D. (2019). Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Computational Biology, 15(6), e1007043. DOI: https://doi.org/10.1371/journal.pcbi.1007043, PMID: 31211783, PMCID: PMC6581260
  32. Qi, H. , Ma, S. , Jia, N. , & Wang, G. (2015). Experiments on individual strategy updating in iterated snowdrift game under random rematching. Journal of Theoretical Biology, 368, 112. DOI: https://doi.org/10.1016/j.jtbi.2014.12.008, PMID: 25542641
  33. Rapoport, A. , & Amaldoss, W. (2000). Mixed strategies and iterative elimination of strongly dominated strategies: An experimental investigation of states of knowledge. Journal of Economic Behavior & Organization, 42(4), 483521. DOI: https://doi.org/10.1016/S0167-2681(00)00101-3
  34. Safra, L. , Chevallier, C. , & Palminteri, S. (2019). Depressive symptoms are associated with blunted reward learning in social contexts. PLoS Computational Biology, 15(7), e1007224. DOI: https://doi.org/10.1371/journal.pcbi.1007224, PMID: 31356594, PMCID: PMC6699715
  35. Set, E. , Saez, I. , Zhu, L. , Houser, D. E. , Myung, N. , Zhong, S. , … Hsu, M. (2014). Dissociable contribution of prefrontal and striatal dopaminergic genes to learning in economic games. Proceedings of the National Academy of Sciences, 111(26), 96159620. DOI: https://doi.org/10.1073/pnas.1316259111, PMID: 24979760, PMCID: PMC4084431
  36. Shiffrin, R. M. , Lee, M. D. , Kim, W. , & Wagenmakers, E.-J. (2008). A survey of model evaluation approaches with a tutorial on hierarchical Bayesian methods. Cognitive Science, 32(8), 12481284. DOI: https://doi.org/10.1080/03640210802414826, PMID: 21585453
  37. Van den Bos, W. , van Dijk, E. , & Crone, E. A. (2012). Learning whom to trust in repeated social interactions: A developmental perspective. Group Processes & Intergroup Relations, 15(2), 243256. DOI: https://doi.org/10.1177/1368430211418698
  38. Van Vliet, D. , De Vugt, M. , Bakker, C. , Pijnenburg, Y. , Vernooij-Dassen, M. , Koopmans, R. , & Verhey, F. (2013). Time to diagnosis in young-onset dementia as compared with late-onset dementia. Psychological Medicine, 43(2), 423432. DOI: https://doi.org/10.1017/S0033291712001122, PMID: 22640548
  39. Watanabe, S. (2010). Asymptotic equivalence of Bayes cross valida tion and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 35713594.
  40. Wiecki, T. V. , Sofer, I. , & Frank, M. J. (2013). HDDM: Hierarchical Bayesian estimation of the drift-diffusion model in Python. Frontiers in Neuroinformatics, 7, 14. DOI: https://doi.org/10.3389/fninf.2013.00014, PMID: 23935581, PMCID: PMC3731670
  41. Wilcox, N. T. (2006). Theories of learning in games and heterogeneity bias. Econometrica, 74(5), 12711292. DOI: https://doi.org/10.1111/j.1468-0262.2006.00704.x
  42. Yoshida, W. , Seymour, B. , Friston, K. J. , & Dolan, R. J. (2010). Neural mechanisms of belief inference during cooperative games. Journal of Neuroscience, 30(32), 1074410751. DOI: https://doi.org/10.1523/JNEUROSCI.5895-09.2010, PMID: 20702705, PMCID: PMC2967416
  43. Zhu, L. , Jiang, Y. , Scabini, D. , Knight, R. T. , & Hsu, M. (2019). Patients with basal ganglia damage show preserved learning in an economic game. Nature Communications, 10(1), 802. DOI: https://doi.org/10.1038/s41467-019-08766-1, PMID: 30778070, PMCID: PMC6379550
  44. Zhu, L. , Mathewson, K. E. , & Hsu, M. (2012). Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning. Proceedings of the National Academy of Sciences, 109(5), 14191424. DOI: https://doi.org/10.1073/pnas.1116783109, PMID: 22307594, PMCID: PMC3277161
  45. Zhu, L. , Walsh, D. , & Hsu, M. (2012). Neuroeconomic measures of social decision-making across the lifespan. Frontiers in Neuroscience, 6, 128. DOI: https://doi.org/10.3389/fnins.2012.00128, PMID: 23049494, PMCID: PMC3448294
Language: English
Submitted on: Feb 20, 2020
Accepted on: Aug 18, 2020
Published on: Nov 1, 2020
Published by: MIT Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Zhihao Zhang, Saksham Chandra, Andrew Kayser, Ming Hsu, Joshua L. Warren, published by MIT Press
This work is licensed under the Creative Commons Attribution 4.0 License.