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Aging and the neuroeconomics of decision making: A review

Abstract

Neuroeconomics refers to a combination of paradigms derived from neuroscience, psychology, and economics for the study of decision making and is an area that has received considerable scientific attention in the recent literature. Using realistic laboratory tasks, researchers seek to study the neurocognitive processes underlying economic decision making and outcome-based decision learning, as well as individual differences in these processes and the social and affective factors that modulate them. To this point, one question has remained largely unanswered: What happens to decision-making processes and their neural substrates during aging? After all, aging is associated with neurocognitive change, which may affect outcome-based decision making. In our study, we use the subjective expected utility model—a well-established decision-making model in economics—as a descriptive framework. After a short survey of the brain areas and neurotransmitter systems associated with outcome-based decision making—and of the effects of aging thereon—we review a number of decision-making studies. Their general data pattern indicates that the decision-making process is changed by age: The elderly perform less efficiently than younger participants, as demonstrated, for instance, by the smaller total rewards that the elderly acquire in lab tasks. These findings are accounted for in terms of age-related deficiencies in the probability and value parameters of the subjective expected utility model. Finally, we discuss some implications and suggestions for future research.

References

  1. Allen, J. S., Bruss, J., Brown, C. K., & Damasio, H. (2005). Methods for studying the aging brain: Volumetric analyses versus VBM. Neurobiology of Aging, 26, 1275–1278. doi:10.1016/j.neurobiolaging.2005.05.017

    Google Scholar 

  2. Allison, T., Puce, A., & McCarthy, G. (2000). Social perception from visual cues: Role of the STS region. Trends in Cognitive Sciences, 4, 267–278. doi:10.1016/S1364-6613(00)01501-1

    PubMed  Google Scholar 

  3. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7, 268–277.

    PubMed  Google Scholar 

  4. Bäckman, L., Nyberg, L., Lindenberger, U., Li, S.-C., & Farde, L. (2006). The correlative triad among aging, dopamine, and cognition: Current status and future prospects. Neuroscience & Biobehavioral Reviews, 30, 791–807. doi:10.1016/j.neubiorev.2006.06.005

    Google Scholar 

  5. Balleine, B. W., Delgado, M. R., & Hikosaka, O. (2007). The role of the dorsal striatum in reward and decision-making. Journal of Neuroscience, 27, 8161–8165. doi:10.1523/JNEUROSCI.1554-07.2007

    PubMed  Google Scholar 

  6. Band, G. P. H., Ridderinkhof, K. R., & Segalowitz, S. (2002). Explaining neurocognitive aging: Is one factor enough? Brain & Cognition, 49, 259–267. doi:10.1006/brcg.2001.1499

    Google Scholar 

  7. Baron-Cohen, S., Ring, H. A., Wheelwright, S., Bullmore, E. T., Brammer, M. J., Simmons, A., & Williams, S. C. R. (1999). Social intelligence in the normal and autistic brain: An fMRI study. European Journal of Neuroscience, 11, 1891–1898.

    PubMed  Google Scholar 

  8. Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games & Economic Behavior, 52, 336–372. doi:10.1016/j.geb.2004.06.010

    Google Scholar 

  9. Bechara, A., Damasio, A. R., Damasio, H., & 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

    PubMed  Google Scholar 

  10. Bhatt, M., & Camerer, C. F. (2005). Self-referential thinking and equilibrium as states of mind in games: fMRI evidence. Games & Economic Behavior, 52, 424–459. doi:10.1016/j.geb.2005.03.007

    Google Scholar 

  11. Braver, T. S., & Barch, D. M. (2002). A theory of cognitive control, ageing cognition, and neuromodulation. Neuroscience & Biobehavioral Reviews, 26, 809–817.

    Google Scholar 

  12. Brunet, E., Sarfati, Y., Hardy-Baylé, M.-C., & Decety, J. (2000). PET investigation of the attribution of intentions with a nonverbal task. NeuroImage, 11, 157–166. doi:10.1006/nimg.1999.0525

    PubMed  Google Scholar 

  13. Calder, A. J., Lawrence, A. D., & Young, A. W. (2001). Neuropsychology of fear and loathing. Nature Reviews Neuroscience, 2, 352–363.

    PubMed  Google Scholar 

  14. Carstensen, L. L. (1992). Social and emotional patterns in adulthood: Support for socioemotional selectivity theory. Psychology & Aging, 7, 331–338.

    Google Scholar 

  15. Castelli, F., Happé, F., Frith, U., & Frith, C. (2000). Movement and mind: A functional imaging of perception and interpretation of complex intentional movement patterns. NeuroImage, 12, 314–325. doi:10.1006/nimg.2000.0612

    PubMed  Google Scholar 

  16. Chamberlain, S. R., Müller, U., Blackwell, A. D., Clark, L., Robbins, T. W., & Sahakian, B. J. (2006). Neurochemical modulation of response inhibition and probabilistic learning in humans. Science, 311, 861–863. doi:10.1126/science.1121218

    PubMed  Google Scholar 

  17. Chasseigne, G., Ligneau, C., Grau, S., Le Gall, A., Roque, M., & Mullet, E. (2004). Aging and probabilistic learning in single- and multiple-cue tasks. Experimental Aging Research, 30, 23–45.

    PubMed  Google Scholar 

  18. Chou, K.-L., Lee, T. M. C., & Ho, A. H. Y. (2007). Does mood state change risk taking tendency in older adults? Psychology & Aging, 22, 310–318. doi:10.1037/0882-7974.22.2.310

    Google Scholar 

  19. Cools, R., Clark, L., Owen, A. M., & Robbins, T. W. (2002). Defining the neural mechanisms of probabilistic reversal learning using eventrelated functional magnetic resonance imaging. Journal of Neuroscience, 22, 4563–4567.

    PubMed  Google Scholar 

  20. Cools, R., Roberts, A. C., & Robbins, T. W. (2008). Serotoninergic regulation of emotional and behavioural control processes. Trends in Cognitive Sciences, 12, 31–40. doi:10.1016/j.tics.2007.10.011

    PubMed  Google Scholar 

  21. Cools, R., Robinson, O. J., & Sahakian, B. (2008). Acute tryptophan depletion in healthy volunteers enhances punishment prediction but does not affect reward prediction. Neuropsychopharmacology, 33, 2291–2299. doi:10.1038/sj.npp.1301598

    PubMed  Google Scholar 

  22. Cox, K. M., Aizenstein, H. J., & Fiez, J. A. (2008). Striatal outcome processing in healthy aging. Cognitive, Affective, & Behavioral Neuroscience, 8, 304–317. doi:10.3758/CABN.8.3.304

    Google Scholar 

  23. Critchley, H. D., Elliott, R., Mathias, C. J., & Dolan, R. J. (2000). Neural activity relating to generation of galvanic skin conductance responses: A functional magnetic resonance imaging study. Journal of Neuroscience, 20, 3033–3040.

    PubMed  Google Scholar 

  24. Cromwell, H. C., & Schultz, W. (2003). Effects of expectations for different reward magnitudes on neuronal activity in primate striatum. Journal of Neurophysiology, 89, 2823–2838. doi:10.1152/ jn.01014.2002

    PubMed  Google Scholar 

  25. Curtis, C. E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7, 415–423. doi:10.1016/S1364-6613(03)00197-9

    PubMed  Google Scholar 

  26. Dawes, C. T., Fowler, J. H., Johnson, T., McElreath, R., & Smirnov, O. (2007). Egalitarian motives in humans. Nature, 446, 794–796. doi:10.1038/nature05651

    PubMed  Google Scholar 

  27. Deakin, J., Aitken, M., Robbins, T., & Sahakian, B. J. (2004). Risk taking during decision-making in normal volunteers changes with age. Journal of the International Neuropsychological Society, 10, 590–598. doi:10.1017/S1355617704104104

    PubMed  Google Scholar 

  28. Delgado, M. R., Frank, R. H., & Phelps, E. A. (2005). Perceptions of moral character modulate the neural systems of reward during the trust game. Nature Neuroscience, 8, 1611–1618. doi:10.1038/nn1575

    PubMed  Google Scholar 

  29. Denburg, N. L., Bechara, A., & Damasio, A. R. (2005). The ability to decide advantageously declines prematurely in some normal older persons. Neuropsychologia, 43, 1099–1106. doi:10.1016/j.neuropsychologia.2004.09.012

    PubMed  Google Scholar 

  30. Denburg, N. L., Cole, C. A., Hernandez, M., Yamada, T. H., Tranel, D., Bechara, A., & Wallace, R. B. (2007). The orbitofrontal cortex, real-world decision-making, and normal aging. Annals of the New York Academy of Sciences, 1121, 480–498. doi:10.1196/ annals.1401.031

    PubMed  Google Scholar 

  31. Denburg, N. L., Recknor, E. C., Bechara, A., & Tranel, D. (2006). Psychophysiological anticipation of positive outcomes promotes advantageous decision-making in normal older persons. International Journal of Psychophysiology, 61, 19–25. doi:10.1016/j.ijpsycho.2005.10.021

    PubMed  Google Scholar 

  32. Derbyshire, S. W. G., Jones, A. K. P., Gyulai, F., Clark, S., Townsend, D., & Firestone, L. L. (1997). Pain processing during three levels of noxious stimulation produces differential patterns of central activity. Pain, 73, 431–445.

    PubMed  Google Scholar 

  33. Doya, K. (2008). Modulators of decision-making. Nature Neuroscience, 11, 410–416.

    PubMed  Google Scholar 

  34. Fein, G., McGillivray, S., & Finn, P. (2007). Older adults make less advantageous decisions than younger adults: Cognitive and psychological correlates. Journal of the International Neuropsychological Society, 13, 480–489. doi:10.1017/S135561770707052X

    PubMed  Google Scholar 

  35. Fera, F., Weickert, T. W., Goldberg, T. E., Tessitore, A., Hariri, A., Das, S., et al. (2005). Neural mechanisms underlying probabilistic category learning in normal aging. Journal of Neuroscience, 25, 11340–11348. doi:10.1523/JNEUROSCI.2736-05.2005

    PubMed  Google Scholar 

  36. Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003). Discrete coding of reward probability and uncertainty by dopamine neurons. Science, 299, 1898–1902. doi:10.1126/science.1077349

    PubMed  Google Scholar 

  37. Forstmann, B. U., Jahfari, S., Scholte, H. S., Wolfensteller, U., van den Wildenberg, W. P. M., & Ridderinkhof, K. R. (2008). Function and structure of the right inferior frontal cortex predict individual differences in response inhibition: A model-based approach. Journal of Neuroscience, 28, 9790–9796. doi:10.1523/ JNEUROSCI.1465-08.2008

    PubMed  Google Scholar 

  38. Frank, M. J., & Kong, L. (2008). Learning to avoid in older age. Psychology & Aging, 23, 392–398. doi:10.1037/0882-7974.23.2.392

    Google Scholar 

  39. Frank, M. J., & O’Reilly, R. C. (2006). A mechanistic account of striatal dopamine function in human cognition: Psychopharmalogical with cabergoline and haloperidol. Behavioral Neuroscience, 120, 497–517. doi:10.1037/0735-7044.120.3.497

    PubMed  Google Scholar 

  40. Frank, M. J., Seeberger, L. C., & O’Reilly, R. C. (2004). By carrot or by stick: Cognitive reinforcement learning in Parkinsonism. Science, 306, 1940–1943. doi:10.1126/science.1102941

    PubMed  Google Scholar 

  41. Gallagher, H. L., & Frith, C. D. (2002). Functional imaging of “theory of mind”. Trends in Cognitive Sciences, 7, 77–83.

    Google Scholar 

  42. Gallagher, H. L., Happé, F., Brunswick, N., Fletcher, P. C., Frith, U., & Frith, C. D. (2000). Reading the mind in cartoons and stories: An fMRI study of “theory of the mind” in verbal and nonverbal tasks. Neuropsychologia, 38, 11–21. doi:10.1016/S0028-3932(99)00053-6

    PubMed  Google Scholar 

  43. Glimcher, P. W., & Rustichini, A. (2004). Neuroeconomics: The consilience of brain and decision. Science, 306, 447–452.

    PubMed  Google Scholar 

  44. Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuro-Image, 14, 21–36. doi:10.1006/nimg.2001.0786

    PubMed  Google Scholar 

  45. Grieve, S. M., Williams, L. M., Paul, R. H., Clark, C. R., & Gordon, E.(2007). Cognitive aging, executive function, and fractional anisotropy: A diffusion tensor MR imaging study. American Journal of Neuroradiology, 28, 226–235.

    PubMed  Google Scholar 

  46. Güroğlu, B., Haselager, G. J. T., van Lieshout, C. F. M., Takashima, A., Rijpkema, M., & Fernández, G. (2008). Why are friends special? Implementing a social interaction simulation task to probe the neural correlates of friendship. NeuroImage, 39, 903–910. doi:10.1016/j.neuroimage.2007.09.007

    PubMed  Google Scholar 

  47. Harbaugh, W. T., Mayr, U., & Burghart, D. R. (2007). Neural responses to taxation and voluntary giving reveal motives for charitable donations. Science, 316, 1622–1625. doi:10.1126/science.1140738

    PubMed  Google Scholar 

  48. Haruno, M., & Kawato, M. (2006). Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning. Journal of Neurophysiology, 95, 948–959. doi:10.1152/jn.00382.2005

    PubMed  Google Scholar 

  49. Heuninckx, S., Wenderoth, N., Debaere, F., Peeters, R., & Swinnen, S. P. (2005). Neural basis of aging: The penetration of cognition into action control. Journal of Neuroscience, 25, 6787–6796. doi:10.1523/JNEUROSCI.1263-05.2005

    PubMed  Google Scholar 

  50. Hikosaka, O., Bromberg-Martin, E., Hong, S., & Matsumoto, M. (2008). New insights on the subcortical representation of reward. Current Opinion in Neurobiology, 18, 203–208. doi:10.1016/j.conb.2008.07.002

    PubMed  Google Scholar 

  51. Holroyd, C. B., & Coles, M. G. H. (2002). The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679–709. doi:10.1037/0033-295X.109.4.679

    PubMed  Google Scholar 

  52. Isella, V., Mapelli, C., Morielli, N., Pelati, O., Franceschi, M., & Appollonio, I. M. (2008). Age-related quantitative and qualitative changes in decision making ability. Behavioral Neurology, 19, 59–63.

    PubMed  Google Scholar 

  53. Juncos-Rabadán, O., Pereiro, A. X., & Facal, D. (2008). Cognitive interference and aging: Insights from a spatial stimulus-response consistency task. Acta Psychologica, 127, 237–246. doi:10.1016/j.actpsy.2007.05.003

    PubMed  Google Scholar 

  54. Kaasinen, V., & Rinne, J. O. (2002). Functional imaging studies of dopamine system and cognition in normal aging and Parkinson’s disease. Neuroscience & Biobehavioral Reviews, 26, 785–793.

    Google Scholar 

  55. Kaasinen, V., Vilkman, H., Hietala, J., Någnen, K., Helenius, H., Olsson, H., et al. (2000). Age-related dopamine D2/D3 receptor loss in extrastriatal regions of the human brain. Neurobiology of Aging, 21, 683–688.

    PubMed  Google Scholar 

  56. King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., & Montague, P. R. (2005). Getting to know you: Reputation and trust in a two-person economic exchange. Science, 308, 78–83. doi:10.1126/science.1108062

    PubMed  Google Scholar 

  57. Knight, M., Seymour, T. L., Gaunt, J. T., Baker, C., Nesmith, K., & Mather, M. (2007). Aging and goal-directed emotional attention: Distraction reverses emotional biases. Emotion, 7, 705–714. doi:10.1037/1528-3542.7.4.705

    PubMed  Google Scholar 

  58. Knutson, B., & Cooper, J. C. (2005). Functional magnetic resonance imaging of reward prediction. Current Opinion in Neurology, 18, 411–417.

    PubMed  Google Scholar 

  59. Knutson, B., Taylor, J., Kaufman, M., Peterson, R., & Glover, G. (2005). Distributed neural representation of expected value. Journal of Neuroscience, 25, 4806–4812. doi:10.1523/ JNEUROSCI.0642-05.2005

    PubMed  Google Scholar 

  60. Knutson, B., Westdorp, A., Kaiser, E., & Hommer, D. (2000). fMRI visualization of brain activity during a monetary incentive delay task. NeuroImage, 12, 20–27. doi:10.1006/nimg.2000.0593

    PubMed  Google Scholar 

  61. Kovalchik, S., Camerer, C. F., Grether, D. M., Plott, C. R., & Allman, J. M. (2005). Aging and decision making: A comparison between neurologically healthy elderly and young individuals. Journal of Economic Behavior & Organization, 58, 79–94. doi:10.1016/j.jebo.2003.12.001

    Google Scholar 

  62. Krueger, F., McCabe, K., Moll, J., Kriegeskorte, N., Zahn, R., Strenziok, M., et al. (2007). Neural correlates of trust. Proceedings of the National Academy of Sciences, 104, 20084–20089. doi:10.1073/ pnas.0710103104

    Google Scholar 

  63. Lamar, M., & Resnick, S. M. (2004). Aging and prefrontal functions: Dissociating orbitofrontal and dorsolateral abilities. Neurobiology of Aging, 25, 553–558. doi:10.1016/j.neurobiolaging.2003.06.005

    PubMed  Google Scholar 

  64. Leclerc, C. M., & Kensinger, E. A. (2008). Age-related differences in medial prefrontal activation in response to emotional images. Cognitive, Affective, & Behavioral Neuroscience, 8, 153–164. doi:10.3758/ CABN.8.2.153

    Google Scholar 

  65. Lee, T. M. C., Leung, A. W. S., Fox, P. T., Gao, J.-H., & Chan, C. C. H. (2008). Age-related differences in neural activities during risk taking as revealed by functional MRI. Social Cognitive & Affective Neuroscience, 3, 7–15. doi:10.1093/scan/nsm033

    Google Scholar 

  66. Li, S.-C., Biele, G., Mohr, P. N. C., & Heekeren, H. R. (2007). Aging and neuroeconomics: Insights from research on neuromodulation of reward-based decision making. Analyse & Kritik, 29, 97–111.

    Google Scholar 

  67. Li, S.-C., Lindenberger, U., & Sikström, S. (2001). Aging cognition: From neuromodulation to representation. Trends in Cognitive Sciences, 5, 479–486.

    PubMed  Google Scholar 

  68. MacPherson, S. E., Philips, L. H., & Della Sala, S. (2002). Age, executive function, and social decision making: A dorsolateral prefrontal theory of cognitive aging. Psychology & Aging, 17, 598–609. doi:10.1037/0882-7974.17.4.598

    Google Scholar 

  69. Marschner, A., Mell, T., Wartenburger, I., Villringer, A., Reischies, F. M., & Heekeren, H. R. (2005). Reward-based decision- making and aging. Brain Research Bulletin, 67, 382–390. doi:10.1016/j.brainresbull.2005.06.010

    PubMed  Google Scholar 

  70. Mata, R., Schooler, L. J., & Rieskamp, J. (2007). The aging decision maker: Cognitive aging and the adaptive selection of decision strategies. Psychology & Aging, 22, 796–810. doi:10.1037/0882-7974.22.4.796

    Google Scholar 

  71. Mather, M. (2006). A review of decision making processes: Weighing the risks and benefits of aging. In L. L. Carstensen & C. R. Hartel (Eds.), When I’m 64: Committee on aging frontiers in social psychology, personality, and adult developmental psychology (pp. 145–173). Washington, DC: National Academies Press.

    Google Scholar 

  72. Mather, M., & Knight, M. (2005). Goal-directed memory: The role of cognitive control in older adults’ emotional memory. Psychology & Aging, 20, 554–570. doi:10.1037/0882-7974.20.4.554

    Google Scholar 

  73. Matochik, J. A., Chefer, S. I., Lane, M. A., Woolf, R. I., Morris, E. D., Ingram, D. K., et al. (2000). Age-related decline in striatal volume in monkeys as measured by magnetic resonance imaging. Neurobiology of Aging, 21, 591–598. doi:10.1016/S0197-4580(00)00134-2

    PubMed  Google Scholar 

  74. Matthews, S. C., Simmons, A. N., Lane, S. D., & Paulus, M. P. (2004). Selective activation of the nucleus accumbens during risktaking decision making. Brain Imaging, 15, 2123–2127.

    Google Scholar 

  75. McCabe, K., Houser, D., Ryan, L., Smith, V., & Trouard, T. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. Proceedings of the National Academy of Sciences, 98, 11832–11835. doi:10.1073/pnas.211415698

    Google Scholar 

  76. Mell, T., Heekeren, H. R., Marschner, A., Wartenburger, I., Villringer, A., & Reischies, F. M. (2005). Effect of aging on stimulus- reward association learning. Neuropsychologica, 43, 554–563. doi:10.1016/j.neuropsychologia.2004.07.010

    Google Scholar 

  77. Montague, P. R., & Berns, G. S. (2002). Neural economics and the biological substrates of valuation. Neuron, 36, 265–284.

    PubMed  Google Scholar 

  78. Montague, P. R., & Lohrenz, T. (2007). To detect and correct: Norm violations and their enforcement. Neuron, 56, 14–18. doi:10.1016/j.neuron.2007.09.020

    PubMed  Google Scholar 

  79. Nieuwenhuis, S., Ridderinkhof, K. R., Talsma, D., Coles, M. G. H., Holroyd, C. B., Kok, A., & van der Molen, M. W. (2002). A computational account of altered error processing in older age: Dopamine and the error-related negativity. Cognitive, Affective, & Behavioral Neuroscience, 2 19–36.

    Google Scholar 

  80. Pan, W.-X., Schmidt, R., Wickens, J. R., & Hyland, B. I. (2005). Dopamine cells respond to predicted events during classical conditioning: Evidence for eligibility traces in the reward-learning network. Journal of Neuroscience, 25, 6235–6242. doi:10.1523/ JNEUROSCI.1478-05.2005

    PubMed  Google Scholar 

  81. Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9, 545–556. doi:10.1038/nrn2357

    PubMed  Google Scholar 

  82. Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., et al. (2005). Regional brain changes in aging healthy adults: General trends, individual differences and modifiers. Cerebral Cortex, 15, 1676–1689. doi:10.1093/cercor/bh1044

    PubMed  Google Scholar 

  83. Raz, N., Rodrigue, K. M., Kennedy, K. M., Head, D., Gunning-Dixon, F., & Acker, J. D. (2003). Differential aging of the human striatum: Longitudinal evidence. American Journal of Neuroradiology, 24, 1849–1856.

    PubMed  Google Scholar 

  84. Raz, N., Williamson, A., Gunning-Dixon, F., Head, D., & Acker, J. D. (2000). Neuroanatomical and cognitive correlates of adult age differences in acquisition of a perceptual-motor skill. Neuroimaging & Memory, 51, 85–93.

    Google Scholar 

  85. Resnick, S. M., Lamar, M., & Driscoll, I. (2007). Vulnerability of the orbitofrontal cortex to age-associated structural and functional brain changes. Annals of the New York Academy of Sciences, 1121, 562–575. doi:10.1196/annals.1401.027

    PubMed  Google Scholar 

  86. Reuter-Lorenz, P. A. (2002). New visions of the aging mind and brain. Trends in Cognitive Sciences, 6, 394–400. doi:10.1016/s1364-6613(02)01957-5

    PubMed  Google Scholar 

  87. Reuter-Lorenz, P. A., & Lustig, C. (2005). Brain aging: Reorganizing discoveries about the aging mind. Current Opinion in Neurobiology, 15, 245–251. doi:10.1016/j.conb.2005.03.016

    PubMed  Google Scholar 

  88. Rilling, J. K., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). The neural correlates of theory of mind within interpersonal reactions. NeuroImage, 22, 1694–1703. doi:10.1016/j.neuroimage.2004.04.015

    PubMed  Google Scholar 

  89. Rogers, R. D., Tunbridge, E. M., Bhagwagar, Z., Drevets, W. C., Sahakian, B. J., & Carter, C. S. (2003). Tryptophan depletion alters the decision-making of healthy volunteers through altered processing of reward cues. Neuropsychopharmacology, 28, 153–162. doi:10.1038/sj.npp.1300001

    PubMed  Google Scholar 

  90. Rolls, E. T. (2004). The functions of the orbitofrontal cortex. Brain & Cognition, 55, 11–29. doi:10.1016/S0278-2626(03)00277-X

    Google Scholar 

  91. Ruffman, T., Henry, J. D., Livingstone, V., & Philips, L. H. (2008). A meta-analytic review of emotion recognition and aging: Implications for neuropsychological models of aging. Neuroscience & Biobehavioral Reviews, 32, 863–881. doi:10.1016/j.neubiorev.2008.01.001

    Google Scholar 

  92. Samanez-Larkin, G. R., Gibbs, S. E. B., Khanna, K., Nielsen, L., Carstensen, L. L., & Knutson, B. (2007). Anticipation of monetary gain but not loss in healthy older adults. Nature Neuroscience, 10, 787–791. doi:10.1038/nn1894

    PubMed  Google Scholar 

  93. Sanfey, A. G. (2007a). Decision neuroscience: New directions in studies of judgement and decision making. Current Directions in Psychological Science, 16, 151–155. doi:10.1111/j.1467.8721.2007.00494.x

    Google Scholar 

  94. Sanfey, A. G. (2007b). Social decision-making: Insights from game theory and neuroscience. Science, 318, 598–602. doi:10.1126/ science.1142996

    PubMed  Google Scholar 

  95. Sanfey, A. G., Loewenstein, G., McClure, S. M., & Cohen, J. D. (2006). Neuroeconomics: Cross-currents in research on decisionmaking. Trends in Cognitive Sciences, 10, 108–116. doi:10.1016/j.tics.2006.01.009

    PubMed  Google Scholar 

  96. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision- making in the Ultimatum Game. Science, 300, 1755–1758. doi:10.1126/ science.1082976

    PubMed  Google Scholar 

  97. Saxe, R. (2006). Uniquely human social cognition. Current Opinion in Neurobiology, 16, 235–239. doi:10.1016/j.conb.2006.03.001

    PubMed  Google Scholar 

  98. Schmitt-Eliassen, J., Ferstl, R., Wiesner, C., Deuschl, G., & Witt, K. (2007). Feedback-based versus observational classification learning in healthy aging and Parkinson’s disease. Brain Research, 1142, 178–188. doi:10.1016/j.brainres.2007.01.042

    PubMed  Google Scholar 

  99. Schott, B. H., Niehaus, L., Wittmann, B. C., Schütze, H., Seidenbecher, C. I., Heinze, H.-J., & Düzel, E. (2007). Ageing and early-stage Parkinson’s disease affect separable neural mechanisms of mesolimbic reward processing. Brain, 130, 2412–2424. doi:10.1093/ brain/awm147

    PubMed  Google Scholar 

  100. Schultz, W. (2000). Multiple reward signals in the brain. Nature Reviews Neuroscience, 1, 199–209.

    PubMed  Google Scholar 

  101. Schultz, W. (2002). Getting formal with dopamine and reward. Neuron, 36, 241–263.

    PubMed  Google Scholar 

  102. Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599. doi:10.1126/ science.275.5306.1593

    PubMed  Google Scholar 

  103. Schweighofer, N., Tanaka, S. C., & Doya, K. (2007). Serotonin and the evaluation of future rewards: Theory, experiments, and possible neural mechanisms. Annals of the New York Academy of Sciences, 1104, 289–300. doi:10.1196/annals.1390.011

    PubMed  Google Scholar 

  104. Shohamy, D., Myers, C. E., Grossman, S., Sage, J., Gluck, M. A., & Poldrack, R. A. (2004). Cortico-striatal contributions to feedbackbased learning: Converging data from neuroimaging and neuropsychology. Brain, 127, 851–859. doi:10.1093/brain/ahw100

    PubMed  Google Scholar 

  105. Slessor, G., Phillips, L. H., & Bull, R. (2007). Exploring the specificity of age-related differences in theory of mind tasks. Psychology & Aging, 22, 639–643. doi:10.1037/0882-7974.22.3.639

    Google Scholar 

  106. Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6, 309–315. doi:10.1038/nn1008

    PubMed  Google Scholar 

  107. Stout, J. C., Rodawalt, W. C., & Siemers, E. R. (2001). Risky decision making in Huntington’s disease. Journal of the International Neuropsychological Society, 7, 92–101. doi:10.1017/s1355617701711095

    PubMed  Google Scholar 

  108. Suri, R. E., Bargas, J., & Arbib, M. A. (2001). Modeling functions of striatal dopamine modulation in learning and planning. Neuroscience, 103, 65–85.

    PubMed  Google Scholar 

  109. Sutter, M., & Kocher, M. G. (2007). Trust and trustworthiness across different age groups. Games & Economic Behavior, 59, 364–382. doi:10.1016/j.geb.2006.07.006

    Google Scholar 

  110. Tanaka, S. C., Doya, K., Okada, G., Ueda, K., Okamoto, Y., & Yamawaki, S. (2004). Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nature Neuroscience, 7, 887–893. doi:10.1038/nn1279

    PubMed  Google Scholar 

  111. Tobler, P. N., Fiorillo, C. D., & Schultz, W. (2005). Adaptive coding of reward value by dopamine neurons. Science, 307, 1642–1645. doi:10.1126/science.1105370

    PubMed  Google Scholar 

  112. Tsuchida, J., Kubo, N., & Kojima, S. (2002). Position reversal learning in aged Japanese macaques. Behavioural Brain Research, 129, 107–112. doi:10.1016/so166-4328(01)00336-9

    PubMed  Google Scholar 

  113. van Winden, F., Stallen, M., & Ridderinkhof, K. R. (2008). On the nature, modeling, and neural bases of social ties. In D. Houser & K. McCabe (Eds.), Neuroeconomics (pp. 125–159). Bingley, U.K.: Emerald.

    Google Scholar 

  114. Walhovd, K. B., Fjell, A. M., Reinvang, I., Lundervold, A., Dale, A. M., Eilertsen, D. E., et al. (2005). Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiology of Aging, 26, 1261–1270. doi:10.1016/j.neurobiolaging.2005.05.020

    PubMed  Google Scholar 

  115. Wang, G. J., Volkow, N. D., Logan, J., Fowler, J. S., Schlyer, D., MacGregor, R. R., et al. (1995). Evaluation of age-related changes in serotonin 5-HT2 and dopamine D2 receptor availability in healthy human subjects. Life Sciences, 56, L249-L253.

    Google Scholar 

  116. Weiler, J. A., Bellebaum, C., & Daum, I. (2008). Aging affects acquisition and reversal of reward-based associative learning. Learning & Memory, 15, 190–197. doi:10.1101/lm890408

    Google Scholar 

  117. Wood, S., Busemeyer, J., Koling, A., Davis, H., & Cox, C. R. (2005). Older adults as adaptive decision-makers: Evidence from the Iowa Gambling Task. Psychology & Aging, 20, 220–225. doi:10.1037/0882-7974.20.2.220

    Google Scholar 

  118. Woodruff-Pak, D. S. (1997). The neuropsychology of aging. Cambridge, MA: Blackwell.

    Google Scholar 

  119. Zacks, R. T., Radvansky, G., & Hasher, L. (1996). Studies of directed forgetting in older adults. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22, 143–156.

    Google Scholar 

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Correspondence to K. Richard Ridderinkhof.

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This research was supported by a VICI Grant from the Netherlands Organization for Scientific Research to K.R.R.

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Brown, S.B.R.E., Ridderinkhof, K.R. Aging and the neuroeconomics of decision making: A review. Cognitive, Affective, & Behavioral Neuroscience 9, 365–379 (2009). https://doi.org/10.3758/CABN.9.4.365

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Keywords

  • Anterior Cingulate Cortex
  • Ventral Striatum
  • Reversal Learning
  • Dorsal Striatum
  • Avoidance Learning