The United Nations. World Population Prospects: The 2006 Revision (UN, 2007).
Hayutin, A. M. Global demographic shifts create challenges and opportunities. PREA Quart. (Fall), 46–53 (2007).
Park, D. C. & Schwarz, N. Cognitive Aging: A Primer 1st edn (Psychology Press, 1999).
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).
Carstensen, L. L. The influence of a sense of time on human development. Science 312, 1913–1915 (2006).
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).
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).
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).
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).
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).
Nielsen, L. & Mather, M. Emerging perspectives in social neuroscience and neuroeconomics of aging. Soc. Cogn. Affect. Neurosci. 6, 149–164 (2011).
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).
Braver, T. S. et al. Mechanisms of motivation–cognition interaction: challenges and opportunities. Cogn. Affect. Behav. Neurosci. 14, 443–472 (2014).
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).
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).
Wundt, W. Outlines of Psychology (Wilhelm Engelmann, 1897).
Russell, J. A. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980).
Bradley, M. M. in Handbook of Psychophysiology (eds Cacioppo, J. T., Tassinary, L. G. & Berntson, G. G.) 602–642 (Cambridge Univ. Press, 2000).
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).
Knutson, B., Katovich, K. & Suri, G. Inferring affect from fMRI data. Trends Cogn. Sci. 18, 422–428 (2014).
Knutson, B., Rick, S., Wimmer, G. E., Prelec, D. & Loewenstein, G. Neural predictors of purchases. Neuron 53, 147–156 (2007).
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).
Palminteri, S. et al. Critical roles for anterior insula and dorsal striatum in punishment-based avoidance learning. Neuron 76, 998–1009 (2012).
Knutson, B. & Greer, S. M. Anticipatory affect: neural correlates and consequences for choice. Phil. Trans. R. Soc. B 363, 3771–3786 (2008).
Sanfey, A. G. Social decision-making: insights from game theory and neuroscience. Science 318, 598–602 (2007).
Levy, D. J. & Glimcher, P. W. The root of all value: a neural common currency for choice. Curr. Opin. Neurobiol. 22, 1027–1038 (2012).
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).
Alexander, W. H. & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neurosci. 14, 1338–1344 (2011).
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).
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).
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).
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).
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).
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).
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).
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.
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).
Mesulam, M. M. & Mufson, E. J. in Cerebral Cortex Vol. 4 (eds Peters, A & Jones, E. G.) 179–226 (Springer, 1985).
Phelps, E. A., Lempert, K. M. & Sokol-Hessner, P. Emotion and decision making: multiple modulatory neural circuits. Annu. Rev. Neurosci. 37, 263–287 (2014).
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).
Hedden, T. & Gabrieli, J. D. E. Insights into the ageing mind: a view from cognitive neuroscience. Nature Rev. Neurosci. 5, 87–96 (2004).
Grady, C. The cognitive neuroscience of ageing. Nature Rev. Neurosci. 13, 491–505 (2012).
Neumann, von, J. & Morgenstern, O. Theory of Games and Economic Behavior (Princeton Univ. Press, 1953).
Bandura, A. Social Learning Theory (General Learning Corporation, 1971).
Knutson, B., Taylor, J., Kaufman, M., Peterson, R. & Glover, G. Distributed neural representation of expected value. J. Neurosci. 25, 4806–4812 (2005).
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).
Loewenstein, G., Rick, S. & Cohen, J. D. Neuroeconomics. Annu. Rev. Psychol. 59, 647–672 (2008).
Nielsen, L., Knutson, B. & Carstensen, L. L. Affect dynamics, affective forecasting, and aging. Emotion 8, 318–330 (2008).
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.
Knutson, B. & Cooper, J. C. Functional magnetic resonance imaging of reward prediction. Curr. Opin. Neurol. 18, 411–417 (2005).
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).
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.
Cox, K. M., Aizenstein, H. J. & Fiez, J. A. Striatal outcome processing in healthy aging. Cogn. Affect. Behav. Neurosci. 8, 304–317 (2008).
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.
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.
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).
Castle, E. et al. Neural and behavioral bases of age differences in perceptions of trust. Proc. Natl Acad. Sci. USA 109, 20848–20852 (2012).
Harlé, K. M. & Sanfey, A. G. Social economic decision-making across the lifespan: an fMRI investigation. Neuropsychologia 50, 1416–1424 (2012).
Markowitz, H. Portfolio selection. J. Finance 7, 77–91 (1952).
Kahneman, D. & Tversky, A. Prospect theory: an analysis of decision under risk. Econometrica 47, 263–291 (1979).
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).
Mather, M. A. in When I'm 64 (eds Carstensen, L. L. & Hartel, C. R.) 145–173 (The National Academies Press, 2006).
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.
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.
Knutson, B. & Bossaerts, P. Neural antecedents of financial decisions. J. Neurosci. 27, 8174–8177 (2007).
Wu, C. C., Sacchet, M. D. & Knutson, B. Toward an affective neuroscience account of financial risk taking. Front. Neurosci. 6, 159 (2012).
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).
McCarrey, A. C. et al. Age differences in neural activity during slot machine gambling: an fMRI study. PLoS ONE 7, e49787 (2012).
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.
Cabeza, R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol. Aging 17, 85–100 (2002).
Reuter-Lorenz, P. A. & Cappell, K. A. Neurocognitive aging and the compensation hypothesis. Curr. Dir. Psychol. Sci. 17, 177–182 (2008).
Kuhnen, C. M. & Knutson, B. The neural basis of financial risk taking. Neuron 47, 763–770 (2005).
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).
Li, Lindenberger, U. & Sikström, S. Aging cognition: from neuromodulation to representation. Trends Cogn. Sci. 5, 479–486 (2001).
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).
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).
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).
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).
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).
Frederick, S., Loewenstein, G. & O'Donoghue, T. Time discounting and time preference: a critical review. J. Econ. Lit. 40, 351–401 (2002).
Berns, G. S., Laibson, D. & Loewenstein, G. Intertemporal choice — toward an integrative framework. Trends Cogn. Sci. 11, 482–488 (2007).
Peters, J. & Büchel, C. The neural mechanisms of inter-temporal decision-making: understanding variability. Trends Cogn. Sci. 15, 227–239 (2011).
Löckenhoff, C. E. Age, time, and decision making: from processing speed to global time horizons. Ann. NY Acad. Sci. 1235, 44–56 (2011).
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).
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).
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).
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).
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).
Kable, J. W. & Glimcher, P. W. The neural correlates of subjective value during intertemporal choice. Nature Neurosci. 10, 1625–1633 (2007).
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).
Ballard, K. & Knutson, B. Dissociable neural representations of future reward magnitude and delay during temporal discounting. Neuroimage 45, 143–150 (2009).
Figner, B. et al. Lateral prefrontal cortex and self-control in intertemporal choice. Nature Neurosci. 13, 538–539 (2010).
Peters, J. & Büchel, C. Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron 66, 138–148 (2010).
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.
Samanez-Larkin, G. R. et al. Age differences in striatal delay sensitivity during intertemporal choice in healthy adults. Front. Neurosci. 5, 126 (2011).
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).
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.
Gilbert, R. J. et al. Risk, reward, and decision-making in a rodent model of cognitive aging. Front. Neurosci. 5, 144 (2011).
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).
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).
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).
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).
Frank, M. J. & Kong, L. Learning to avoid in older age. Psychol. Aging 23, 392–398 (2008).
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).
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.
Eppinger, B., Kray, J., Mock, B. & Mecklinger, A. Better or worse than expected? Aging, learning, and the ERN. Neuropsychologia 46, 521–539 (2008).
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.
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.
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).
Braver, T. S. & Barch, D. M. A theory of cognitive control, aging cognition, and neuromodulation. Neurosci. Biobehav. Rev. 26, 809–817 (2002).
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).
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).
West, R. L. An application of prefrontal cortex function theory to cognitive aging. Psychol. Bull. 120, 272–292 (1996).
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.
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).
Kwan, D. et al. Future decision-making without episodic mental time travel. Hippocampus 22, 1 215–1219 (2012).
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.
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).
Fjell, A. M. et al. Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiol. Aging 34, 2239–2247 (2013).
Raz, N. & Rodrigue, K. M. Differential aging of the brain: patterns, cognitive correlates and modifiers. Neurosci. Biobehav. Rev. 30, 730–748 (2006).
Bennett, I. J. & Madden, D. J. Disconnected aging: cerebral white matter integrity and age-related differences in cognition. Neuroscience 12, 187–205 (2014).
Sullivan, E. V. & Pfefferbaum, A. Diffusion tensor imaging and aging. Neurosci. Biobehav. Rev. 30, 749–761 (2006).
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).
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).
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).
Segovia, G., Porras, A., Del Arco, A. & Mora, F. Glutamatergic neurotransmission in aging: a critical perspective. Mech. Ageing Dev. 122, 1–29 (2001).
Mora, F., Segovia, G. & Del Arco, A. Glutamate–dopamine–GABA interactions in the aging basal ganglia. Brain Res. Rev. 58, 340–353 (2008).
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).
Mata, R. et al. Ecological rationality: a framework for understanding and aiding the aging decision maker. Front. Neurosci. 6, 19 (2012).
Horn, J. L. & Cattell, R. B. Age differences in fluid and crystallized intelligence. Acta Psychol. 26, 107–129 (1967).
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.
Li, Y. et al. Sound credit scores and financial decisions despite cognitive aging. Proc. Natl Acad. Sci. USA 112, 65–69 (2015).
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).
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).
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).
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).
Worthy, D. A. & Maddox, W. T. Age-based differences in strategy use in choice tasks. Front. Neurosci. 5, 145 (2012).
Mata, R. & Nunes, L. When less is enough: cognitive aging, information search, and decision quality in consumer choice. Psychol. Aging 25, 289–298 (2010).
Lindenberger, U. Human cognitive aging: corriger la fortune? Science 346, 572–578 (2014).
Gutchess, A. Plasticity of the aging brain: new directions in cognitive neuroscience. Science 346, 579–582 (2014).
Knutson, B., Samanez-Larkin, G. R. & Kuhnen, C. M. Gain and loss learning differentially contribute to life financial outcomes. PLoS ONE 6, e24390 (2011).
Denburg, N. L. et al. The orbitofrontal cortex, real-world decision-making, and normal aging. Ann. NY Acad. Sci. 1121, 480–498 (2007).
SaveAndInvest.org Fighting Fraud 101. Save and Invest [online], (2011).
Korniotis, G. M. & Kumar, A. Do older investors make better investment decisions? Rev. Econom. Statist. 93, 244–265 (2011).