Validating the PVL-Delta model for the Iowa gambling task

Frontiers in Psychology

Volumn 4, Issue DEC, 2013, Pages

  Steingroever, Helen ^a   Wetzels, Ruud ^a,b   Wagenmakers, Eric Jan ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b Royal Netherlands Academy for Arts and Sciences   (Netherlands)

Author keywords

Expectancy valence model; Parameter space partitioning; Prospect valence model; Reinforcement learning; Test of selective influence

Indexed keywords

EID: 84891720120     PISSN: None     EISSN: 16641078     Source Type: Journal    
DOI: 10.3389/fpsyg.2013.00898     Document Type: Article

References (25)

1. Ahn, W.-Y., Busemeyer, J. R., Wagenmakers, E.-J., and Stout, J. C. (2008). Comparison of decision learning models using the generalization criterion method. Cogn. Sci. 32, 1376-1402. doi: 10.1080/03640210802352992.
2. Ahn, W.-Y., Krawitz, A., Kim, W., Busemeyer, J. R., and Brown, J. W. (2011). A model-based fMRI analysis with hierarchical Bayesian parameter estimation. J. Neurosci. Psychol. Econ. 4, 95-110. doi: 10.1037/a0020684.
3. Bechara, A., Damasio, A. R., Damasio, H., and Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7-15. doi: 10.1016/0010-0277(94)90018-3.
4. Bechara, A., Damasio, H., Tranel, D., and Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science 275, 1293-1295. doi: 10.1126/science.275.5304.1293.
5. Busemeyer, J. R., and Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychol. Assess. 14, 253-262. doi: 10.1037/1040-3590.14.3.253.
6. Busemeyer, J. R., Stout, J., and Finn, P. (in press). "Using computational models to help explain decision making processes of substance abusers," in Cognitive and Affective Neuroscience of Psychopathology, ed D. Barch (New York, NY: Oxford University Press).
7. Fridberg, D. J., Queller, S., Ahn, W.-Y., Kim, W., Bishara, A. J., Busemeyer, J. R., et al. (2010). Cognitive mechanisms underlying risky decision-making in chronic cannabis users. J. Math. Psychol. 54, 28-38. doi: 10.1016/j.jmp.2009.10.002.
8. Gelman, A., and Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457-472. doi: 10.1214/ss/1177011136.
9. Hoffman, M. D., and Gelman, A. (2011). The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Arxiv preprint arXiv:1111.4246.
10. Luce, R. (1959). Individual Choice Behavior. New York, NY: Wiley.
11. Pitt, M., Kim, W., Navarro, D., and Myung, J. (2006). Global model analysis by parameter space partitioning. Psychol. Rev. 113, 57-83. doi: 10.1037/0033-295X.113.1.57.
12. Pitt, M., Myung, J., Montenegro, M., and Pooley, J. (2008). Measuring model flexibility with parameter space partitioning: an introduction and application example. Cogn. Sci. 32, 1285-1303. doi: 10.1080/03640210802477534.
13. Stan Development Team. (2013a). Stan: A C++ Library for Probability and Sampling, Version 2.0. Available online at: http://mc-stan.org/.
14. Stan Development Team. (2013b). Stan Modeling Language User's Guide and Reference Manual, Version 2.0. Available online at: http://mc-stan.org/.
15. Steingroever, H., Wetzels, R., Horstmann, A., Neumann, J., and Wagenmakers, E.-J. (2013a). Performance of healthy participants on the Iowa gambling task. Psychol. Assess. 25, 180-193. doi: 10.1037/a0029929.
16. Steingroever, H., Wetzels, R., and Wagenmakers, E.-J. (2013b). A comparison of reinforcement-learning models for the Iowa gambling task using parameter space partitioning. J. Prob. Solv. 5:2. doi: 10.7771/1932-6246.1150.
17. Steingroever, H., Wetzels, R., and Wagenmakers, E.-J. (in press). Absolute performance of reinforcement-learning models for the Iowa gambling task. Decision.
18. Tversky, A., and Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertain. 5, 297-323. doi: 10.1007/BF00122574.
19. Vanpaemel, W. (2009). "Measuring model complexity with the prior predictive," in Advances in Neural Information Processing Systems 22, eds Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, 1919-1927 (Red Hook, NY: Curran Associates Inc.).
20. Wetzels, R., Vandekerckhove, J., Tuerlinckx, F., and Wagenmakers, E.-J. (2010). Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task. J. Math. Psychol. 54, 14-27. doi: 10.1016/j.jmp.2008.12.001.
21. Worthy, D. A., Hawthorne, M. J., and Otto, A. R. (2013). Heterogeneity of strategy use in the Iowa gambling task: a comparison of win-stay/lose-shift and reinforcement learning models. Psychon. Bull. Rev. 20, 364-371. doi: 10.3758/s13423-012-0324-9.
22. Yechiam, E., and Busemeyer, J. (2005). Comparison of basic assumptions embedded in learning models for experience-based decision making. Psychon. Bull. Rev. 12, 387-402. doi: 10.3758/BF03193783.
23. Yechiam, E., and Busemeyer, J. R. (2008). Evaluating generalizability and parameter consistency in learning models. Games Econ. Behav. 63, 370-394. doi: 10.1016/j.geb.2007.08.011.
24. Yechiam, E., and Ert, E. (2007). Evaluating the reliance on past choices in adaptive learning models. J. Math. Psychol. 51, 75-84. doi: 10.1016/j.jmp.2006.11.002.
25. Zhao, J., and Costello, F. (2007). Computational modelling of switching behaviour in repeated gambles. Artif. Intell. Rev. 27, 209-222. doi: 10.1007/s10462-008-9083-4.