References
- 1Ahn, W.-Y., Busemeyer, J. R., Wagenmakers, E.-J., & Stout, J. C. (2008). Comparison of decision learning models using the generalization criterion method. Cognitive Science, 32(8), 1376–1402. DOI: 10.1080/03640210802352992
- 2Ahn, W. Y., Dai, J., Vassileva, J., Busemeyer, J. R., & Stout, J. C. (2016).
Computational modeling for addiction medicine . In Progress in Brain Research, 224, 53–65. Elsevier. DOI: 10.1016/bs.pbr.2015.07.032 - 3Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package. Computational Psychiatry, 1(0), 24. DOI: 10.1162/CPSY_a_00002
- 4Ahn, W.-Y., Vasilev, G., Lee, S.-H., Busemeyer, J. R., Kruschke, J. K., Bechara, A., & Vassileva, J. (2014). Decision-making in stimulant and opiate addicts in protracted abstinence: Evidence from computational modeling with pure users. Frontiers in Psychology, 5. DOI: 10.3389/fpsyg.2014.00849
- 5Alacreu-Crespo, A., Guillaume, S., Sénèque, M., Olié, E., & Courtet, P. (2020). Cognitive modelling to assess decision-making impairments in patients with current depression and with/without suicide history. European Neuropsychopharmacology, 36, 50–59. DOI: 10.1016/j.euroneuro.2020.04.006
- 6Almy, B., Kuskowski, M., Malone, S. M., Myers, E., & Luciana, M. (2018). A longitudinal analysis of adolescent decision-making with the Iowa Gambling Task. Developmental Psychology, 54(4), 689–702. DOI: 10.1037/dev0000460
- 7Baeza-Velasco, C. (2020). Decision-making in major depressive disorder_ Subjective complaint, objective performance, and discrepancy between both. Journal of Affective Disorders, 6. DOI: 10.1016/j.jad.2020.03.064
- 8Bechara, A. (2007). Iowa gambling task professional manual. Lutz: Psychological Assessment Resources.
- 9Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1–3), 7–15. DOI: 10.1016/0010-0277(94)90018-3
- 10Bishop, S. J., & Gagne, C. (2018). Anxiety, Depression, and Decision Making: A Computational Perspective. Annual Review of Neuroscience, 41(1), 371–388. DOI: 10.1146/annurev-neuro-080317-062007
- 11Brown, V. M., Chen, J., Gillan, C. M., & Price, R. B. (2020). Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(6), 601–609. DOI: 10.1016/j.bpsc.2019.12.019
- 12Brown, V. M., Chen, J., Gillan, C. M., & Price, R. B. (2021). Improving the reliability of computational analyses: Model-based planning and its relationship with compulsivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(6), 601–609. DOI: 10.1016/j.bpsc.2019.12.019
- 13Buelow, M. T., & Barnhart, W. R. (2018). Test–Retest Reliability of Common Behavioral Decision Making Tasks. Archives of Clinical Neuropsychology, 33(1), 125–129. DOI: 10.1093/arclin/acx038
- 14Buelow, M. T., & Suhr, J. A. (2009). Construct Validity of the Iowa Gambling Task. Neuropsychology Review, 19(1), 102–114. DOI: 10.1007/s11065-009-9083-4
- 15Buelow, M. T., & Suhr, J. A. (2013). Personality characteristics and state mood influence individual deck selections on the Iowa Gambling Task. Personality and Individual Differences, 54(5), 593–597. DOI: 10.1016/j.paid.2012.11.019
- 16Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14(3), 253–262. DOI: 10.1037/1040-3590.14.3.253
- 17Byrne, K. A., Norris, D. D., & Worthy, D. A. (2016). Dopamine, depressive symptoms, and decision-making: The relationship between spontaneous eye blink rate and depressive symptoms predicts Iowa Gambling Task performance. Cognitive, Affective, & Behavioral Neuroscience, 16(1), 23–36. DOI: 10.3758/s13415-015-0377-0
- 18Carpenter, B., Gelman, A., Hoffman, M., & Lee, D. (2016). Stan: A probabilistic programming language. Journal of Statistical Software, 76. DOI: 10.18637/jss.v076.i01
- 19Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: The BIS/BAS Scales. Journal of Personality and Social Psychology, 67(2), 319–333. DOI: 10.1037/0022-3514.67.2.319
- 20Case, J. A. C., & Olino, T. M. (2020). Approach and avoidance patterns in reward learning across domains: An initial examination of the Social Iowa Gambling Task. Behaviour Research and Therapy, 125, 103547. DOI: 10.1016/j.brat.2019.103547
- 21Cauffman, E., Shulman, E. P., Steinberg, L., Claus, E., Banich, M. T., Graham, S., & Woolard, J. (2010). Age differences in affective decision making as indexed by performance on the Iowa Gambling Task. Developmental Psychology, 46(1), 193–207. DOI: 10.1037/a0016128
- 22Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., … Rose, M. (2007). The Patient-Reported Outcomes Measurement Information System (PROMIS). Medical Care, 45(5), S3–S11. DOI: 10.1097/01.mlr.0000258615.42478.55
- 23Cella, M., Dymond, S., & Cooper, A. (2010). Impaired flexible decision-making in major depressive disorder. Journal of Affective Disorders, 124(1–2), 207–210. DOI: 10.1016/j.jad.2009.11.013
- 24Chen, C., Takahashi, T., Nakagawa, S., Inoue, T., & Kusumi, I. (2015). Reinforcement learning in depression: A review of computational research. Neuroscience & Biobehavioral Reviews, 55, 247–267. DOI: 10.1016/j.neubiorev.2015.05.005
- 25Chen, G., Pine, D. S., Brotman, M. A., Smith, A. R., Cox, R. W., & Haller, S. P. (2021). Trial and error: A hierarchical modeling approach to test-retest reliability. NeuroImage, 245, 118647. DOI: 10.1016/j.neuroimage.2021.118647
- 26Christakou, A., Gershman, S. J., Niv, Y., Simmons, A., Brammer, M., & Rubia, K. (2013). Neural and Psychological Maturation of Decision-making in Adolescence and Young Adulthood. Journal of Cognitive Neuroscience, 25(11), 1807–1823. DOI: 10.1162/jocn_a_00447
- 27Chung, D., Kadlec, K., Aimone, J. A., McCurry, K., King-Casas, B., & Chiu, P. H. (2017). Valuation in major depression is intact and stable in a non-learning environment. Scientific Reports, 7(1), 44374. DOI: 10.1038/srep44374
- 28Clark, L., & Watson, D. (1991). Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. Journal of Abnormal Psychology, 100(3), 316-336. DOI: 10.1037/0021-843X.100.3.316
- 29Cooper, S. R., Gonthier, C., Barch, D. M., & Braver, T. S. (2017). The role of psychometrics in individual differences research in cognition: A case study of the AX-CPT. Frontiers in Psychology, 8, 1482. DOI: 10.3389/fpsyg.2017.01482
- 30Dombrovski, A. Y., & Hallquist, M. N. (2021). Search for solutions, learning, simulation, and choice processes in suicidal behavior. WIREs Cognitive Science. DOI: 10.1002/wcs.1561
- 31Ducasse, D., Loas, G., Dassa, D., Gramaglia, C., Zeppegno, P., Guillaume, S., … Courtet, P. (2018). Anhedonia is associated with suicidal ideation independently of depression: A meta-analysis. Depression and Anxiety, 35(5), 382–392. DOI: 10.1002/da.22709
- 32Frank, M. J., Seeberger, L. C., & O’Reilly, R. C. (2004). By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism. Science, New Series, 306(5703), 1940–1943. DOI: 10.1126/science.1102941
- 33Gershman, S. J. (2015). Do learning rates adapt to the distribution of rewards? Psychonomic Bulletin & Review, 22(5), 1320–1327. DOI: 10.3758/s13423-014-0790-3
- 34Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472. DOI: 10.2307/2246093
- 35Jollant, F. (2016). Prefrontal inositol levels and implicit decision-making in healthy individuals and depressed patients. European Neuropsychopharmacology, 26, 1255–1263. DOI: 10.1016/j.euroneuro.2016.06.005
- 36Haines, N., Kvam, P. D., Irving, L. H., Smith, C., Beauchaine, T. P., Pitt, M. A., Ahn, W.-Y., & Turner, B. (2020). Theoretically informed generative models can advance the psychological and brain sciences: Lessons from the reliability paradox [Preprint]. PsyArXiv. DOI: 10.31234/osf.io/xr7y3
- 37Haines, N., Vassileva, J., & Ahn, W.-Y. (2018). The Outcome-Representation Learning model: A novel reinforcement learning model of the Iowa Gambling Task. Cognitive Science, 42(8), 2534–2561. DOI: 10.1111/cogs.12688
- 38Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50(3), 1166–1186. DOI: 10.3758/s13428-017-0935-1
- 39Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013). Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. Biology of Mood & Anxiety Disorders, 3(1), 12. DOI: 10.1186/2045-5380-3-12
- 40Katahira, K. (2016). How hierarchical models improve point estimates of model parameters at the individual level. Journal of Mathematical Psychology, 73, 37–58. DOI: 10.1016/j.jmp.2016.03.007
- 41Khazanov, G. K., & Ruscio, A. M. (2016). Is Low Positive Emotionality a Specific Risk Factor for Depression? A Meta-Analysis of Longitudinal Studies. Psychological Bulletin, 142(9), 991–1015. DOI: 10.1037/bul0000059.supp
- 42Kildahl, N., Hansen, S., Brevers, D., & Skewes, J. (2020). Individual differences in learning during decision-making may predict specific harms associated with gambling. Addictive Behaviors, 110, 106496. DOI: 10.1016/j.addbeh.2020.106496
- 43Kvam, P. D., Romeu, R. J., Turner, B. M., Vassileva, J., & Busemeyer, J. R. (2021). Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks. Psychological Methods, 26(1), 18–37. DOI: 10.1037/met0000264
- 44Lejuez, C. W., Aklin, W. M., Jones, H. A., Richards, J. B., Strong, D. R., Kahler, C. W., & Read, J. P. (2003). The balloon analogue risk task (BART) differentiates smokers and nonsmokers. Experimental and Clinical Psychopharmacology, 11(1), 26–33. DOI: 10.1037/1064-1297.11.1.26
- 45Lin, C.-H., Chiu, Y.-C., Lee, P.-L., & Hsieh, J.-C. (2007). Is deck B a disadvantageous deck in the Iowa Gambling Task? Behavioral and Brain Functions, 3(1), 16. DOI: 10.1186/1744-9081-3-16
- 46Ly, 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. 467–479). John Wiley & Sons, Ltd. DOI: 10.1002/9781119159193.ch34 - 47McGovern, A. R., Alexopoulos, G. S., Yuen, G. S., Morimoto, S. S., & Gunning-Dixon, F. M. (2014). Reward-related decision making in older adults: Relationship to clinical presentation of depression: Decision making in older adults with late-life depression. International Journal of Geriatric Psychiatry, 29(11), 1125–1131. DOI: 10.1002/gps.4200
- 48Miu, A. C., Heilman, R. M., & Houser, D. (2008). Anxiety impairs decision-making: Psychophysiological evidence from an Iowa Gambling Task. Biological Psychology, 77(3), 353–358. DOI: 10.1016/j.biopsycho.2007.11.010
- 49Moniz, M., Neves de Jesus, S., Gonçalves, E., Pacheco, A., & Viseu, J. (2016). Decision-making in adult unipolar depressed patients and healthy subjects: Significant differences in Net Score and in non-traditional alternative measures. Neuropsychological Trends, 19, 7–15. DOI: 10.7358/neur-2016-019-moni
- 50Moutoussis, M., Bullmore, E. T., Goodyer, I. M., Fonagy, P., Jones, P. B., Dolan, R. J., & Dayan, P. (2018). Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood. PLOS Computational Biology, 14(12),
e1006679 . DOI: 10.1371/journal.pcbi.1006679 - 51Mueller, E. M., Nguyen, J., Ray, W. J., & Borkovec, T. D. (2010). Future-oriented decision-making in Generalized Anxiety Disorder is evident across different versions of the Iowa Gambling Task. Journal of Behavior Therapy and Experimental Psychiatry, 41(2), 165–171. DOI: 10.1016/j.jbtep.2009.12.002
- 52Mukherjee, D., & Kable, J. W. (2014). Value-based decision making in mental illness: A meta-analysis. Clinical Psychological Science, 2(6), 767–782. DOI: 10.1177/2167702614531580
- 53Must, A., Szabó, Z., Bódi, N., Szász, A., Janka, Z., & Kéri, S. (2006). Sensitivity to reward and punishment and the prefrontal cortex in major depression. Journal of Affective Disorders, 90(2–3), 209–215. DOI: 10.1016/j.jad.2005.12.005
- 54Nussenbaum, K., & Hartley, C. A. (2019). Reinforcement learning across development: What insights can we draw from a decade of research? Developmental Cognitive Neuroscience, 40, 100733. DOI: 10.1016/j.dcn.2019.100733
- 55Parsons, S., Kruijt, A.-W., & Fox, E. (2019). Psychological Science Needs a Standard Practice of Reporting the Reliability of Cognitive-Behavioral Measurements. Advances in Methods and Practices in Psychological Science, 2(4), 378–395. DOI: 10.1177/2515245919879695
- 56Paulus, M. P., & Yu, A. J. (2012). Emotion and decision-making: Affect-driven belief systems in anxiety and depression. Trends in Cognitive Sciences, 16(9), 476–483. DOI: 10.1016/j.tics.2012.07.009
- 57Peters, E., & Slovic, P. (2000). The springs of action: Affective and analytical information processing in choice. Personality and Social Psychology Bulletin, 26(12), 1465–1475. DOI: 10.1177/01461672002612002
- 58Pizzagalli, D. A., Smoski, M., Ang, Y.-S., Whitton, A. E., Sanacora, G., Mathew, S. J., Nurnberger, J., Lisanby, S. H., Iosifescu, D. V., Murrough, J. W., Yang, H., Weiner, R. D., Calabrese, J. R., Goodman, W., Potter, W. Z., & Krystal, A. D. (2020). Selective kappa-opioid antagonism ameliorates anhedonic behavior: Evidence from the Fast-fail Trial in Mood and Anxiety Spectrum Disorders (FAST-MAS). Neuropsychopharmacology, 45(10), 1656–1663. DOI: 10.1038/s41386-020-0738-4
- 59Price, R. B., Brown, V., & Siegle, G. J. (2019). Computational modeling applied to the dot-probe task yields improved reliability and mechanistic insights. Biological Psychiatry, 85(7), 606–612. DOI: 10.1016/j.biopsych.2018.09.022
- 60Rinaldi, R., Lefebvre, L., Joachim, A., & Rossignol, M. (2020). Decision-making of patients with major depressive disorder in the framework of action control. Cognitive Neuropsychiatry, 25(1), 71–83. DOI: 10.1080/13546805.2019.1685481
- 61Romeu, R. J., Haines, N., Ahn, W.-Y., Busemeyer, J. R., & Vassileva, J. (2020). A computational model of the Cambridge gambling task with applications to substance use disorders. Drug and Alcohol Dependence, 206, 107711. DOI: 10.1016/j.drugalcdep.2019.107711
- 62Rouder, J. N., & Haaf, J. M. (2019). A psychometrics of individual differences in experimental tasks. Psychonomic Bulletin & Review, 26(2), 452–467. DOI: 10.3758/s13423-018-1558-y
- 63Schmitz, F., Kunina-Habenicht, O., Hildebrandt, A., Oberauer, K., & Wilhelm, O. (2020). Psychometrics of the Iowa and Berlin Gambling Tasks: Unresolved Issues With Reliability and Validity for Risk Taking. Assessment, 27(2), 232–245. DOI: 10.1177/1073191117750470
- 64Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-prime software. (Pittsburgh, PA) Version 2.0.
- 65Shahar, N., Hauser, T. U., Moutoussis, M., Moran, R., Keramati, M., NSPN consortium, & Dolan, R. J. (2019). Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling. PLOS Computational Biology, 15(2),
e1006803 . DOI: 10.1371/journal.pcbi.1006803 - 66Smoski, M. J., Lynch, T. R., Rosenthal, M. Z., Cheavens, J. S., Chapman, A. L., & Krishnan, R. R. (2008). Decision-making and risk aversion among depressive adults. Journal of Behavior Therapy and Experimental Psychiatry, 39(4), 567–576. DOI: 10.1016/j.jbtep.2008.01.004
- 67Snaith, R. P., Hamilton, M., Morley, S., Humayan, A., Hargreaves, D., & Trigwell, P. (1995). A scale for the assessment of hedonic tone the Snaith-Hamilton Pleasure Scale. The British Journal of Psychiatry: Journal of Mental Science, 167(1), 99–103. DOI: 10.1192/bjp.167.1.99
- 68Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15, 72–101. DOI: 10.2307/1412159
- 69Stan Development Team. (2017). RStan: the R interface to Stan. R package version 2.16.0.
http://mc-stan.org - 70Suhr, J. A., & Tsanadis, J. (2007). Affect and personality correlates of the Iowa Gambling Task. Personality and Individual Differences, 43(1), 27–36. DOI: 10.1016/j.paid.2006.11.004
- 71Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & Van Maanen, L. (2017). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology, 76, 65–79. DOI: 10.1016/j.jmp.2016.01.001
- 72van Honk, J., Hermans, E. J., Putman, P., Montagne, B., & Schulter, D. J. (2002). Defective somatic markers in subclinical psychopathy. Neuroreport, 13, 1025–1027. DOI: 10.1097/00001756-200206120-00009
- 73Vandekerckhove, J. (2014). A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. Journal of Mathematical Psychology, 60, 58–71. DOI: 10.1016/j.jmp.2014.06.004
- 74Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/3397865 - 75Watson, D., Weber, K., Assenheimer, J. S., Clark, L. A., et al. (1995). Testing a tripartite model: I. Evaluating the convergent and discriminant validity of anxiety and depression symptom scales. Journal of Abnormal Psychology, 104(1), 3–14. DOI: 10.1037//0021-843X.104.1.3
- 76Weiss, N. A. (2019). wBoot R package. Retrieved from
https://CRAN.R-project.org/package=wBoot - 77Worthy, D. A., Pang, B., & Byrne, K. A. (2013). Decomposing the roles of perseveration and expected value representation in models of the Iowa gambling task. Frontiers in Psychology, 4. DOI: 10.3389/fpsyg.2013.00640
- 78Xu, S., Korczykowski, M., Zhu, S., & Rao, H. (2013). Assessment of risk-taking and impulsive behaviors: A comparison between three tasks. Social Behavior and Personality, 41(3), 477–486. DOI: 10.2224/sbp.2013.41.3.477
- 79Yechiam, E., Busemeyer, J. R., Stout, J. C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychological Science, 16(12), 973–978. DOI: 10.1111/j.1467-9280.2005.01646.x
