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Decision making in the ageing brain: changes in affective and motivational circuits

Nature Reviews Neuroscience volume 16, pages 278–289 (2015)Cite this article

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Key Points

  • Research has begun to explore how age-related changes in the brain systems that are implicated in affect and motivation influence decision making.

  • Older and younger adults show similar affective and neural sensitivity to anticipated financial gains during value assessment as well as to gain and loss outcomes. However, older adults show reduced affective and neural sensitivity to anticipated financial losses.

  • Older adults make more suboptimal choices during financial risk taking, which seems to be related not to shifts in risk preference but rather to increased variability in nucleus accumbens (NAc) activity.

  • Older adults make more optimal choices during delay discounting by assigning higher values to future gains, which appears to be related to increased NAc activity during consideration of future rewards.

  • Older adults make more suboptimal choices when engaging in probabilistic reward learning. This seems to be related to decreased NAc activity associated with reward prediction errors (but not necessarily reward predictions), which may result from reduced medial prefrontal cortex input into striatal circuits.

  • Understanding how ageing variably influences brain function and structure may better inform targeted interventions designed to improve decision performance in individuals of all ages.

Abstract

As the global population ages, older decision makers will be required to take greater responsibility for their own physical, psychological and financial well-being. With this in mind, researchers have begun to examine the effects of ageing on decision making and associated neural circuits. A new 'affect–integration–motivation' (AIM) framework may help to clarify how affective and motivational circuits support decision making. Recent research has shed light on whether and how ageing influences these circuits, providing an interdisciplinary account of how ageing can alter decision making.

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Figure 1: Important components of the affect–integration–motivation framework.
Figure 2: Age-related differences in incentive anticipation and risky decision making.
Figure 3: Age-related differences in temporal decision making and value learning.

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References

  1. The United Nations. World Population Prospects: The 2006 Revision (UN, 2007).

  2. Hayutin, A. M. Global demographic shifts create challenges and opportunities. PREA Quart. (Fall), 46–53 (2007).

  3. Park, D. C. & Schwarz, N. Cognitive Aging: A Primer 1st edn (Psychology Press, 1999).

    Google Scholar 

  4. Carstensen, L. L. et al. Emotional experience improves with age: evidence based on over 10 years of experience sampling. Psychol. Aging 26, 21–33 (2011).

    PubMed  PubMed Central  Google Scholar 

  5. Carstensen, L. L. The influence of a sense of time on human development. Science 312, 1913–1915 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Samanez-Larkin, G. R. & Carstensen, L. L. in The Oxford Handbook of Social Neuroscience Ch. 34 (eds Decety, J. & Cacioppo, J. T.) (Oxford Univ. Press, 2011).

    Google Scholar 

  7. Mohr, P. N. C., Li, S.-C. & Heekeren, H. R. Neuroeconomics and aging: neuromodulation of economic decision making in old age. Neurosci. Biobehav. Rev. 34, 678–688 (2010).

    CAS  PubMed  Google Scholar 

  8. Brown, S. B. R. E. & Ridderinkhof, K. R. Aging and the neuroeconomics of decision making: a review. Cogn. Affect. Behav. Neurosci. 9, 365–379 (2009).

    PubMed  Google Scholar 

  9. Weierich, M. R. et al. Older and wiser? An affective science perspective on age-related challenges in financial decision making. Soc. Cogn. Affect. Neurosci. 6, 195–206 (2011).

    PubMed  Google Scholar 

  10. Eppinger, B., Hämmerer, D. & Li, S.-C. Neuromodulation of reward-based learning and decision making in human aging. Ann. NY Acad. Sci. 1235, 1–17 (2011).

    PubMed  Google Scholar 

  11. Nielsen, L. & Mather, M. Emerging perspectives in social neuroscience and neuroeconomics of aging. Soc. Cogn. Affect. Neurosci. 6, 149–164 (2011).

    PubMed  PubMed Central  Google Scholar 

  12. Hsu, M., Lin, H. & Mcnamara, P. Neuroeconomics of decision making in the aging brain: the example of long-term care. Adv. Health Econ. Health Serv. Res. 20, 203–225 (2008).

    PubMed  Google Scholar 

  13. Braver, T. S. et al. Mechanisms of motivation–cognition interaction: challenges and opportunities. Cogn. Affect. Behav. Neurosci. 14, 443–472 (2014).

    PubMed  PubMed Central  Google Scholar 

  14. Loewenstein, G. & Lerner, J. S. in Handbook of Affective Sciences Ch. 31 (eds Davidson, R. J., Sherer, K. R. & Goldsmith, H. H.) (Oxford Univ. Press, 2003).

    Google Scholar 

  15. Naqvi, N., Shiv, B. & Bechara, A. The role of emotion in decision making: a cognitive neuroscience perspective. Curr. Direct. Psychol. Sci. 15, 260–264 (2006).

    Google Scholar 

  16. Wundt, W. Outlines of Psychology (Wilhelm Engelmann, 1897).

    Google Scholar 

  17. Russell, J. A. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980).

    Google Scholar 

  18. Bradley, M. M. in Handbook of Psychophysiology (eds Cacioppo, J. T., Tassinary, L. G. & Berntson, G. G.) 602–642 (Cambridge Univ. Press, 2000).

    Google Scholar 

  19. Watson, D., Wiese, D., Vaidya, J. & Tellegen, A. The two general activation systems of affect: structural findings, evolutionary considerations, and psychobiological evidence. J. Pers. Soc. Psychol. 76, 820–838 (1999).

    Google Scholar 

  20. Knutson, B., Katovich, K. & Suri, G. Inferring affect from fMRI data. Trends Cogn. Sci. 18, 422–428 (2014).

    PubMed  Google Scholar 

  21. Knutson, B., Rick, S., Wimmer, G. E., Prelec, D. & Loewenstein, G. Neural predictors of purchases. Neuron 53, 147–156 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J. & Frith, C. D. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature 442, 1042–1045 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Palminteri, S. et al. Critical roles for anterior insula and dorsal striatum in punishment-based avoidance learning. Neuron 76, 998–1009 (2012).

    CAS  PubMed  Google Scholar 

  24. Knutson, B. & Greer, S. M. Anticipatory affect: neural correlates and consequences for choice. Phil. Trans. R. Soc. B 363, 3771–3786 (2008).

    PubMed  Google Scholar 

  25. Sanfey, A. G. Social decision-making: insights from game theory and neuroscience. Science 318, 598–602 (2007).

    CAS  PubMed  Google Scholar 

  26. Levy, D. J. & Glimcher, P. W. The root of all value: a neural common currency for choice. Curr. Opin. Neurobiol. 22, 1027–1038 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Rangel, A., Camerer, C. F. & Montague, P. R. A framework for studying the neurobiology of value-based decision making. Nature Rev. Neurosci. 9, 545–556 (2008).

    CAS  Google Scholar 

  28. Alexander, W. H. & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neurosci. 14, 1338–1344 (2011).

    CAS  PubMed  Google Scholar 

  29. Plassmann, H., O'Doherty, J. P., Shiv, B. & Rangel, A. Marketing actions can modulate neural representations of experienced pleasantness. Proc. Natl Acad. Sci. USA 105, 1050–1054 (2008).

    CAS  PubMed  Google Scholar 

  30. Liu, X., Hairston, J., Schrier, M. & Fan, J. Common and distinct networks underlying reward valence and processing stages: a meta-analysis of functional neuroimaging studies. Neurosci. Biobehav. Rev. 35, 1219–1236 (2011).

    PubMed  Google Scholar 

  31. Diekhof, E. K., Kaps, L., Falkai, P. & Gruber, O. The role of the human ventral striatum and the medial orbitofrontal cortex in the representation of reward magnitude — an activation likelihood estimation meta-analysis of neuroimaging studies of passive reward expectancy and outcome processing. Neuropsychologia 50, 1252–1266 (2012).

    PubMed  Google Scholar 

  32. Bartra, O., McGuire, J. T. & Kable, J. W. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage 76, 412–427 (2013).

    PubMed  PubMed Central  Google Scholar 

  33. Clithero, J. A. & Rangel, A. Informatic parcellation of the network involved in the computation of subjective value. Soc. Cogn. Affect. Neurosci. 9, 1289–1302 (2014).

    PubMed  Google Scholar 

  34. Mogenson, G. J., Jones, D. L. & Yim, C. Y. From motivation to action: functional interface between the limbic system and the motor system. Prog. Neurobiol. 14, 69–97 (1980).

    CAS  PubMed  Google Scholar 

  35. Jones, S. R. et al. Profound neuronal plasticity in response to inactivation of the dopamine transporter. Proc. Natl Acad. Sci. USA 95, 4029–4034 (1998).

    CAS  PubMed  Google Scholar 

  36. Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010). This review outlines the evolutionarily conserved neural circuits that are implicated in reward processing, motivation and choice.

    PubMed  Google Scholar 

  37. Chikama, M., McFarland, N. R., Amaral, D. G. & Haber, S. N. Insular cortical projections to functional regions of the striatum correlate with cortical cytoarchitectonic organization in the primate. J. Neurosci. 17, 9686–9705 (1997).

    CAS  PubMed  Google Scholar 

  38. Mesulam, M. M. & Mufson, E. J. in Cerebral Cortex Vol. 4 (eds Peters, A & Jones, E. G.) 179–226 (Springer, 1985).

    Google Scholar 

  39. Phelps, E. A., Lempert, K. M. & Sokol-Hessner, P. Emotion and decision making: multiple modulatory neural circuits. Annu. Rev. Neurosci. 37, 263–287 (2014).

    CAS  PubMed  Google Scholar 

  40. Buckner, R. L. Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 44, 195–208 (2004).

    CAS  PubMed  Google Scholar 

  41. Hedden, T. & Gabrieli, J. D. E. Insights into the ageing mind: a view from cognitive neuroscience. Nature Rev. Neurosci. 5, 87–96 (2004).

    CAS  Google Scholar 

  42. Grady, C. The cognitive neuroscience of ageing. Nature Rev. Neurosci. 13, 491–505 (2012).

    CAS  Google Scholar 

  43. Neumann, von, J. & Morgenstern, O. Theory of Games and Economic Behavior (Princeton Univ. Press, 1953).

    Google Scholar 

  44. Bandura, A. Social Learning Theory (General Learning Corporation, 1971).

    Google Scholar 

  45. Knutson, B., Taylor, J., Kaufman, M., Peterson, R. & Glover, G. Distributed neural representation of expected value. J. Neurosci. 25, 4806–4812 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Yacubian, J. et al. Dissociable systems for gain- and loss-related value predictions and errors of prediction in the human brain. J. Neurosci. 26, 9530–9537 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Loewenstein, G., Rick, S. & Cohen, J. D. Neuroeconomics. Annu. Rev. Psychol. 59, 647–672 (2008).

    PubMed  Google Scholar 

  48. Nielsen, L., Knutson, B. & Carstensen, L. L. Affect dynamics, affective forecasting, and aging. Emotion 8, 318–330 (2008).

    PubMed  PubMed Central  Google Scholar 

  49. Samanez-Larkin, G. R. et al. Anticipation of monetary gain but not loss in healthy older adults. Nature Neurosci. 10, 787–791 (2007). This study demonstrates an asymmetry in which older adults show less neural sensitivity to anticipated losses than younger adults but not to anticipated gains or outcomes.

    PubMed  Google Scholar 

  50. Knutson, B. & Cooper, J. C. Functional magnetic resonance imaging of reward prediction. Curr. Opin. Neurol. 18, 411–417 (2005).

    PubMed  Google Scholar 

  51. Wu, C. C., Samanez-Larkin, G. R., Katovich, K. & Knutson, B. Affective traits link to reliable neural markers of incentive anticipation. Neuroimage 84, 279–289 (2014).

    PubMed  Google Scholar 

  52. Schott, B. H. et al. Ageing and early-stage Parkinson's disease affect separable neural mechanisms of mesolimbic reward processing. Brain 130, 2412–2424 (2007). This study clarifies the differences between healthy ageing and Parkinson disease in terms of neural activity and functional connectivity during the anticipation and receipt of monetary rewards.

    PubMed  Google Scholar 

  53. Cox, K. M., Aizenstein, H. J. & Fiez, J. A. Striatal outcome processing in healthy aging. Cogn. Affect. Behav. Neurosci. 8, 304–317 (2008).

    PubMed  PubMed Central  Google Scholar 

  54. Samanez-Larkin, G. R., Kuhnen, C. M., Yoo, D. J. & Knutson, B. Variability in nucleus accumbens activity mediates age-related suboptimal financial risk taking. J. Neurosci. 30, 1426–1434 (2010). This study identifies a novel measure of neural signal variability that mediates age differences in risky choice during a financial investment task.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Samanez-Larkin, G. R., Worthy, D. A., Mata, R., McClure, S. M. & Knutson, B. Adult age differences in frontostriatal representation of prediction error but not reward outcome. Cogn. Affect. Behav. Neurosci. 14, 672–682 (2014). This study shows that age differences in neural representations of reward prediction error do not result from more basic age differences in reward sensitivity.

    PubMed  PubMed Central  Google Scholar 

  56. Spaniol, J., Bowen, H. J., Wegier, P. & Grady, C. Neural responses to monetary incentives in younger and older adults. Brain Res. http://dx.doi.org/10.1016/j.brainres.2014.09.063 (2014).

  57. Castle, E. et al. Neural and behavioral bases of age differences in perceptions of trust. Proc. Natl Acad. Sci. USA 109, 20848–20852 (2012).

    CAS  PubMed  Google Scholar 

  58. Harlé, K. M. & Sanfey, A. G. Social economic decision-making across the lifespan: an fMRI investigation. Neuropsychologia 50, 1416–1424 (2012).

    PubMed  PubMed Central  Google Scholar 

  59. Markowitz, H. Portfolio selection. J. Finance 7, 77–91 (1952).

    Google Scholar 

  60. Kahneman, D. & Tversky, A. Prospect theory: an analysis of decision under risk. Econometrica 47, 263–291 (1979).

    Google Scholar 

  61. Weber, E. U., Blais, A. R. & Betz, N. E. A domain-specific risk–attitude scale: measuring risk perceptions and risk behaviors. J. Behav. Decis. Making 15, 263–290 (2002).

    Google Scholar 

  62. Mather, M. A. in When I'm 64 (eds Carstensen, L. L. & Hartel, C. R.) 145–173 (The National Academies Press, 2006).

    Google Scholar 

  63. Mata, R., Josef, A. K., Samanez-Larkin, G. R. & Hertwig, R. Age differences in risky choice: a meta-analysis. Ann. NY Acad. Sci. 1235, 18–29 (2011). This quantitative meta-analysis implies that there are age-related performance decrements in learning-dependent compared with non-learning-dependent tasks.

    PubMed  Google Scholar 

  64. Henninger, D. E., Madden, D. J. & Huettel, S. A. Processing speed and memory mediate age-related differences in decision making. Psychol. Aging 25, 262–270 (2010). This study highlights the role of fluid cognitive abilities in decision making throughout adulthood.

    PubMed  PubMed Central  Google Scholar 

  65. Knutson, B. & Bossaerts, P. Neural antecedents of financial decisions. J. Neurosci. 27, 8174–8177 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Wu, C. C., Sacchet, M. D. & Knutson, B. Toward an affective neuroscience account of financial risk taking. Front. Neurosci. 6, 159 (2012).

    PubMed  PubMed Central  Google Scholar 

  67. Lee, T. M. C., Leung, A. W. S., Fox, P. T., Gao, J.-H. & Chan, C. C. H. Age-related differences in neural activities during risk taking as revealed by functional MRI. Soc. Cogn. Affect. Neurosci. 3, 7–15 (2008).

    PubMed  PubMed Central  Google Scholar 

  68. McCarrey, A. C. et al. Age differences in neural activity during slot machine gambling: an fMRI study. PLoS ONE 7, e49787 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Hosseini, S. M. H. et al. Aging and decision making under uncertainty: behavioral and neural evidence for the preservation of decision making in the absence of learning in old age. Neuroimage 52, 1514–1520 (2010). This study demonstrates similar functional neural recruitment across age in risky decisions that do not require recent learning.

    PubMed  Google Scholar 

  70. Cabeza, R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol. Aging 17, 85–100 (2002).

    PubMed  Google Scholar 

  71. Reuter-Lorenz, P. A. & Cappell, K. A. Neurocognitive aging and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182 (2008).

    Google Scholar 

  72. Kuhnen, C. M. & Knutson, B. The neural basis of financial risk taking. Neuron 47, 763–770 (2005).

    CAS  PubMed  Google Scholar 

  73. Samanez-Larkin, G. R., Wagner, A. D. & Knutson, B. Expected value information improves financial risk taking across the adult life span. Soc. Cogn. Affect. Neurosci. 6, 207–217 (2011).

    PubMed  Google Scholar 

  74. Li, Lindenberger, U. & Sikström, S. Aging cognition: from neuromodulation to representation. Trends Cogn. Sci. 5, 479–486 (2001).

    PubMed  Google Scholar 

  75. Garrett, D. D., Kovacevic, N., McIntosh, A. R. & Grady, C. L. Blood oxygen level-dependent signal variability is more than just noise. J. Neurosci. 30, 4914–4921 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Garrett, D. D. et al. Moment-to-moment brain signal variability: a next frontier in human brain mapping? Neurosci. Biobehav. Rev. 37, 610–624 (2013).

    PubMed  PubMed Central  Google Scholar 

  77. Garrett, D. D., McIntosh, A. R. & Grady, C. L. Moment-to-moment signal variability in the human brain can inform models of stochastic facilitation now. Nature Rev. Neurosci. 12, 612 (2011).

    CAS  Google Scholar 

  78. Eppinger, B. & Kray, J. To choose or to avoid: age differences in learning from positive and negative feedback. J. Cogn. Neurosci. 23, 41–52 (2011).

    PubMed  Google Scholar 

  79. Rogalsky, C., Vidal, C., Li, X. & Damasio, H. Risky decision-making in older adults without cognitive deficits: an fMRI study of VMPFC using the Iowa Gambling Task. Soc. Neurosci. 7, 178–190 (2012).

    PubMed  Google Scholar 

  80. Frederick, S., Loewenstein, G. & O'Donoghue, T. Time discounting and time preference: a critical review. J. Econ. Lit. 40, 351–401 (2002).

    Google Scholar 

  81. Berns, G. S., Laibson, D. & Loewenstein, G. Intertemporal choice — toward an integrative framework. Trends Cogn. Sci. 11, 482–488 (2007).

    PubMed  Google Scholar 

  82. Peters, J. & Büchel, C. The neural mechanisms of inter-temporal decision-making: understanding variability. Trends Cogn. Sci. 15, 227–239 (2011).

    PubMed  Google Scholar 

  83. Löckenhoff, C. E. Age, time, and decision making: from processing speed to global time horizons. Ann. NY Acad. Sci. 1235, 44–56 (2011).

    PubMed  Google Scholar 

  84. Simon, N. et al. Good things come to those who wait: attenuated discounting of delayed rewards in aged Fischer 344 rats. Neurobiol. Aging 31, 853–862 (2010).

    PubMed  Google Scholar 

  85. Roesch, M. R., Bryden, D. W., Cerri, D. H., Haney, Z. R. & Schoenbaum, G. Willingness to wait and altered encoding of time-discounted reward in the orbitofrontal cortex with normal aging. J. Neurosci. 32, 5525–5533 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Löckenhoff, C. E., O'Donoghue, T. & Dunning, D. Age differences in temporal discounting: the role of dispositional affect and anticipated emotions. Psychol. Aging 26, 274–284 (2011).

    PubMed  Google Scholar 

  87. McClure, S. M., Laibson, D. I., Loewenstein, G. & Cohen, J. D. Separate neural systems value immediate and delayed monetary rewards. Science 306, 503–507 (2004).

    CAS  PubMed  Google Scholar 

  88. McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G. & Cohen, J. D. Time discounting for primary rewards. J. Neurosci. 27, 5796–5804 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Kable, J. W. & Glimcher, P. W. The neural correlates of subjective value during intertemporal choice. Nature Neurosci. 10, 1625–1633 (2007).

    CAS  PubMed  Google Scholar 

  90. Kable, J. W. & Glimcher, P. W. An 'as soon as possible' effect in human intertemporal decision making: behavioral evidence and neural mechanisms. J. Neurophysiol. 103, 2513–2531 (2010).

    PubMed  PubMed Central  Google Scholar 

  91. Ballard, K. & Knutson, B. Dissociable neural representations of future reward magnitude and delay during temporal discounting. Neuroimage 45, 143–150 (2009).

    PubMed  Google Scholar 

  92. Figner, B. et al. Lateral prefrontal cortex and self-control in intertemporal choice. Nature Neurosci. 13, 538–539 (2010).

    CAS  PubMed  Google Scholar 

  93. Peters, J. & Büchel, C. Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron 66, 138–148 (2010).

    CAS  PubMed  Google Scholar 

  94. Eppinger, B., Nystrom, L. E. & Cohen, J. D. Reduced sensitivity to immediate reward during decision-making in older than younger adults. PLoS ONE 7, e36953 (2012). This study explains older adults' relative patience for delayed rewards by showing a lack of delay-related reduction in neural activity in older age.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Samanez-Larkin, G. R. et al. Age differences in striatal delay sensitivity during intertemporal choice in healthy adults. Front. Neurosci. 5, 126 (2011).

    PubMed  PubMed Central  Google Scholar 

  96. Phillips, P. E. M., Walton, M. E. & Jhou, T. C. Calculating utility: preclinical evidence for cost–benefit analysis by mesolimbic dopamine. Psychopharmacology 191, 483–495 (2006).

    PubMed  Google Scholar 

  97. Li, Y., Baldassi, M., Johnson, E. J. & Weber, E. U. Complementary cognitive capabilities, economic decision making, and aging. Psychol. Aging 28, 595–613 (2013). This study demonstrates that enhanced crystallized cognitive abilities can compensate for diminished fluid cognitive capacities in old age across a range of decision-making tasks.

    PubMed  PubMed Central  Google Scholar 

  98. Gilbert, R. J. et al. Risk, reward, and decision-making in a rodent model of cognitive aging. Front. Neurosci. 5, 144 (2011).

    PubMed  Google Scholar 

  99. Denburg, N. L., Recknor, E. C., Bechara, A. & Tranel, D. Psychophysiological anticipation of positive outcomes promotes advantageous decision-making in normal older persons. Int. J. Psychophysiol. 61, 19–25 (2006).

    PubMed  Google Scholar 

  100. Wood, S., Busemeyer, J., Koling, A., Cox, C. R. & Davis, H. Older adults as adaptive decision makers: evidence from the Iowa Gambling Task. Psychol. Aging 20, 220–225 (2005).

    PubMed  Google Scholar 

  101. Hämmerer, D., Li, S.-C., Müller, V. & Lindenberger, U. Life span differences in electrophysiological correlates of monitoring gains and losses during probabilistic reinforcement learning. J. Cogn. Neurosci. 23, 579–592 (2011).

    PubMed  Google Scholar 

  102. Eppinger, B., Schuck, N. W., Nystrom, L. E. & Cohen, J. D. Reduced striatal responses to reward prediction errors in older compared with younger adults. J. Neurosci. 33, 9905–9912 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Frank, M. J. & Kong, L. Learning to avoid in older age. Psychol. Aging 23, 392–398 (2008).

    PubMed  Google Scholar 

  104. Simon, J. R., Howard, J. H. & Howard, D. V. Adult age differences in learning from positive and negative probabilistic feedback. Neuropsychology 24, 534–541 (2010).

    PubMed  PubMed Central  Google Scholar 

  105. Mell, T. et al. Altered function of ventral striatum during reward-based decision making in old age. Front. Hum. Neurosci. 3, 34 (2009). This study demonstrates that age differences in frontostriatal function are most pronounced during early stages of learning and after reversal of incentive contingencies.

    PubMed  PubMed Central  Google Scholar 

  106. Eppinger, B., Kray, J., Mock, B. & Mecklinger, A. Better or worse than expected? Aging, learning, and the ERN. Neuropsychologia 46, 521–539 (2008).

    PubMed  Google Scholar 

  107. Chowdhury, R. et al. Dopamine restores reward prediction errors in old age. Nature Neurosci. 16, 648–653 (2013). This study uses a dopaminergic precursor (L-DOPA) to enhance reward prediction error-related neural activity in underperforming older adults.

    CAS  PubMed  Google Scholar 

  108. Samanez-Larkin, G. R., Levens, S. M., Perry, L. M., Dougherty, R. F. & Knutson, B. Frontostriatal white matter integrity mediates adult age differences in probabilistic reward learning. J. Neurosci. 32, 5333–5337 (2012). This study shows that the structural connectivity of frontostriatal projections mediates age differences in reward learning.

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Fellows, L. K. Orbitofrontal contributions to value-based decision making: evidence from humans with frontal lobe damage. Ann. NY Acad. Sci. 1239, 51–58 (2011).

    PubMed  Google Scholar 

  110. Braver, T. S. & Barch, D. M. A theory of cognitive control, aging cognition, and neuromodulation. Neurosci. Biobehav. Rev. 26, 809–817 (2002).

    PubMed  Google Scholar 

  111. Bäckman, L., Nyberg, L., Lindenberger, U., Li, S. C. & Farde, L. The correlative triad among aging, dopamine, and cognition: current status and future prospects. Neurosci. Biobehav. Rev. 30, 791–807 (2006).

    PubMed  Google Scholar 

  112. Davis, S. W., Dennis, N. A., Daselaar, S. M., Fleck, M. S. & Cabeza, R. Que PASA? The posterior-anterior shift in aging. Cereb. Cortex 18, 1201–1209 (2008).

    PubMed  Google Scholar 

  113. West, R. L. An application of prefrontal cortex function theory to cognitive aging. Psychol. Bull. 120, 272–292 (1996).

    CAS  PubMed  Google Scholar 

  114. Rubin, D. C. Frontal-striatal circuits in cognitive aging: evidence for caudate involvement. Aging Neuropsychol. Cogn. 6, 241–259 (1999). This early paper on cognitive ageing emphasizes the importance of examining frontostriatal circuits rather than the frontal cortex in isolation.

    Google Scholar 

  115. Benoit, R. G., Gilbert, S. J. & Burgess, P. W. A neural mechanism mediating the impact of episodic prospection on farsighted decisions. J. Neurosci. 31, 6771–6779 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Kwan, D. et al. Future decision-making without episodic mental time travel. Hippocampus 22, 1 215–1219 (2012).

    Google Scholar 

  117. Lighthall, N. R., Huettel, S. A. & Cabeza, R. Functional compensation in the ventromedial prefrontal cortex improves memory-dependent decisions in older adults. J. Neurosci. 34, 15648–15657 (2014). This study shows that increased mPFC activity in older adults promotes improved decision making in cognitively demanding decision tasks.

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Raz, N. in Cognitive Neuroscience of Aging: Linking Cognitive and Cerebral Aging Ch. 2 (eds Cabeza, R., Nyberg, L. & Park, D.) 19–52 (Oxford Univ. Press, 2005).

    Google Scholar 

  119. Fjell, A. M. et al. Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiol. Aging 34, 2239–2247 (2013).

    PubMed  PubMed Central  Google Scholar 

  120. Raz, N. & Rodrigue, K. M. Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci. Biobehav. Rev. 30, 730–748 (2006).

    PubMed  PubMed Central  Google Scholar 

  121. Bennett, I. J. & Madden, D. J. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience 12, 187–205 (2014).

    Google Scholar 

  122. Sullivan, E. V. & Pfefferbaum, A. Diffusion tensor imaging and aging. Neurosci. Biobehav. Rev. 30, 749–761 (2006).

    PubMed  Google Scholar 

  123. Bäckman, L., Lindenberger, U., Li, S.-C. & Nyberg, L. Linking cognitive aging to alterations in dopamine neurotransmitter functioning: recent data and future avenues. Neurosci. Biobehav. Rev. 34, 670–677 (2010).

    PubMed  Google Scholar 

  124. Klostermann, E. C., Braskie, M. N., Landau, S. M., O'Neil, J. P. & Jagust, W. J. Dopamine and frontostriatal networks in cognitive aging. Neurobiol. Aging 33, 623.e15–623.e24 (2012).

    CAS  Google Scholar 

  125. Allard, S., Scardochio, T., Cuello, A. C. & Ribeiro-da-Silva, A. Correlation of cognitive performance and morphological changes in neocortical pyramidal neurons in aging. Neurobiol. Aging 33, 1466–1480 (2012).

    PubMed  Google Scholar 

  126. Segovia, G., Porras, A., Del Arco, A. & Mora, F. Glutamatergic neurotransmission in aging: a critical perspective. Mech. Ageing Dev. 122, 1–29 (2001).

    CAS  PubMed  Google Scholar 

  127. Mora, F., Segovia, G. & Del Arco, A. Glutamate–dopamine–GABA interactions in the aging basal ganglia. Brain Res. Rev. 58, 340–353 (2008).

    CAS  PubMed  Google Scholar 

  128. Dreher, J.-C., Meyer-Lindenberg, A., Kohn, P. & Berman, K. F. Age-related changes in midbrain dopaminergic regulation of the human reward system. Proc. Natl Acad. Sci. USA 105, 15106–15111 (2008).

    CAS  PubMed  Google Scholar 

  129. Mata, R. et al. Ecological rationality: a framework for understanding and aiding the aging decision maker. Front. Neurosci. 6, 19 (2012).

    PubMed  PubMed Central  Google Scholar 

  130. Horn, J. L. & Cattell, R. B. Age differences in fluid and crystallized intelligence. Acta Psychol. 26, 107–129 (1967).

    CAS  Google Scholar 

  131. Agarwal, S., Driscoll, J. C., Gabaix, X. & Laibson, D. I. The age of reason: financial decisions over the life-cycle with implications for regulation. BPEA 40, 51–117 (2009). This study adapts classic findings on age differences in fluid and crystallized abilities to explain adult age differences in optimal financial decisions in the real world.

    Google Scholar 

  132. Li, Y. et al. Sound credit scores and financial decisions despite cognitive aging. Proc. Natl Acad. Sci. USA 112, 65–69 (2015).

    CAS  PubMed  Google Scholar 

  133. Löckenhoff, C. E. & Carstensen, L. L. Aging, emotion, and health-related decision strategies: motivational manipulations can reduce age differences. Psychol. Aging 22, 134–146 (2007).

    PubMed  Google Scholar 

  134. Westbrook, A., Martins, B. S., Yarkoni, T. & Braver, T. S. Strategic insight and age-related goal-neglect influence risky decision-making. Front. Neurosci. 6, 68 (2012).

    PubMed  PubMed Central  Google Scholar 

  135. Reyna, V. F. & Lloyd, F. J. Physician decision making and cardiac risk: effects of knowledge, risk perception, risk tolerance, and fuzzy processing. J. Exp. Psychol. Appl. 12, 179–195 (2006).

    PubMed  Google Scholar 

  136. Reyna, V. F. & Farley, F. Risk and rationality in adolescent decision making: implications for theory, practice, and public policy. Psychol. Sci. Public Interest 7, 1–44 (2006).

    PubMed  Google Scholar 

  137. Worthy, D. A. & Maddox, W. T. Age-based differences in strategy use in choice tasks. Front. Neurosci. 5, 145 (2012).

    PubMed  PubMed Central  Google Scholar 

  138. Mata, R. & Nunes, L. When less is enough: cognitive aging, information search, and decision quality in consumer choice. Psychol. Aging 25, 289–298 (2010).

    PubMed  Google Scholar 

  139. Lindenberger, U. Human cognitive aging: corriger la fortune? Science 346, 572–578 (2014).

    CAS  PubMed  Google Scholar 

  140. Gutchess, A. Plasticity of the aging brain: new directions in cognitive neuroscience. Science 346, 579–582 (2014).

    CAS  PubMed  Google Scholar 

  141. Knutson, B., Samanez-Larkin, G. R. & Kuhnen, C. M. Gain and loss learning differentially contribute to life financial outcomes. PLoS ONE 6, e24390 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Denburg, N. L. et al. The orbitofrontal cortex, real-world decision-making, and normal aging. Ann. NY Acad. Sci. 1121, 480–498 (2007).

    PubMed  Google Scholar 

  143. SaveAndInvest.org Fighting Fraud 101. Save and Invest [online], (2011).

  144. Korniotis, G. M. & Kumar, A. Do older investors make better investment decisions? Rev. Econom. Statist. 93, 244–265 (2011).

    Google Scholar 

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Acknowledgements

Much of the research and ideas mentioned were supported by grants from the US National Institute on Aging (R21AG030778 to B.K. and F31AG032804, F32AG039131 and R00AG042596 to G.R.S-L.) and the FINRA Investor Education Foundation. The authors thank B. Eppinger and three anonymous reviewers for comments on earlier drafts of the manuscript.

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  1. Gregory R. Samanez-Larkin and Brian Knutson: All authors contributed equally to this work.

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  1. Department of Psychology, 2 Hillhouse Avenue, Yale University, New Haven, 06520, Connecticut, USA

    Gregory R. Samanez-Larkin

  2. Department of Psychology, Building 420, Jordan Hall, Stanford University, Stanford, 94305, California, USA

    Brian Knutson

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Correspondence to Brian Knutson.

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PowerPoint slides

Glossary

Fluid cognitive abilities

Abilities to flexibly generate, transform and manipulate new information.

Crystallized cognitive abilities

Abilities to invoke previously stored information that is drawn from experience or accumulated knowledge.

Affect

Emotional responses that include a combination of subjective valence and arousal. Affect is sometimes depicted as a two-dimensional space, in which the two dimensions correspond to valence and arousal.

Cross-sectional studies

Studies that compare individuals (for example, individuals of different ages) at one simultaneous time point.

Longitudinal studies

Studies that compare the same individuals (for example, individuals of different ages) repeatedly over multiple time points to assess change.

Positron emission tomography

(PET). A nuclear imaging technique that produces three-dimensional images of brain activity by detecting photons that are emitted by a positron-emitting radionuclide tracer.

Valence

The subjectively positive or negative feeling evoked by an experience.

Arousal

The subjective level of alertness, activation or energy elicited by an experience.

Functional MRI

(fMRI). A functional imaging technique that uses a magnetic field and radio waves to measure the blood-oxygenation-level-dependent signal, which indexes regional brain activity.

Probabilistic learning

Learning in which individuals use recent feedback to guide future choices among options of uncertain value.

Reward prediction

A quantity denoting the expected reward.

Reward prediction errors

Quantities denoting the difference between the received versus expected reward.

Diffusion tensor imaging

A neuroimaging technique that uses the restricted diffusion of water around neural membranes and myelinated fibres to map anatomical connectivity between brain areas.

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Samanez-Larkin, G., Knutson, B. Decision making in the ageing brain: changes in affective and motivational circuits. Nat Rev Neurosci 16, 278–289 (2015). https://doi.org/10.1038/nrn3917

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