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Amount and time exert independent influences on intertemporal choice

Nature Human Behaviour (2019) | Download Citation


Intertemporal choices involve trade-offs between the value of rewards and the delay before those rewards are experienced. Canonical intertemporal choice models such as hyperbolic discounting assume that reward amount and time until delivery are integrated within each option prior to comparison1,2. An alternative view posits that intertemporal choice reflects attribute-wise processes in which amount and time attributes are compared separately3,4,5,6. Here, we use multi-attribute drift diffusion modelling (DDM) to show that attribute-wise comparison represents the choice process better than option-wise comparison for intertemporal choice in a young adult population. We find that, while accumulation rates for amount and time information are uncorrelated, the difference between those rates predicts individual differences in patience. Moreover, patient individuals incorporate amount earlier than time into the decision process. Using eye tracking, we link these modelling results to attention, showing that patience results from a rapid, attribute-wise process that prioritizes amount over time information. Thus, we find converging evidence that distinct evaluation processes for amount and time determine intertemporal financial choices. Because intertemporal decisions in the lab have been linked to failures of patience ranging from insufficient saving to addiction7,8,9,10,11,12,13, understanding individual differences in the choice process is important for developing more effective interventions.

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  1. 1.

    Samuelson, P. A. A note on measurement of utility. Rev. Econ. Stud. 4, 155–161 (1937).

  2. 2.

    Ainslie, G. Specious reward: a behavioral theory of impulsiveness and impulse control. Psychol. Bull. 82, 463–496 (1975).

  3. 3.

    Roelofsma, P. H. M. P. & Read, D. Intransitive intertemporal choice. J. Behav. Decis. Mak. 13, 161–177 (2000).

  4. 4.

    Read, D., Frederick, S. & Scholten, M. DRIFT: an analysis of outcome framing in intertemporal choice. J. Exp. Psychol. Learn. Mem. Cogn. 39, 573–588 (2013).

  5. 5.

    Dai, J. & Busemeyer, J. R. A probabilistic, dynamic, and attribute-wise model of intertemporal choice. J. Exp. Psychol. Gen. 143, 1489–1514 (2014).

  6. 6.

    Ericson, K. M., White, J. M., Laibson, D. & Cohen, J. D. Money earlier or later? Simple heuristics explain intertemporal choices better than delay discounting does. Psychol. Sci. 26, 826–833 (2015).

  7. 7.

    Story, G. W., Vlaev, I., Seymour, B., Darzi, A. & Dolan, R. J. Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective. Front. Behav. Neurosci. 8, 76 (2014).

  8. 8.

    Lempert, K. M. & Phelps, E. A. The malleability of intertemporal choice. Trends. Cogn. Sci. 20, 64–74 (2015).

  9. 9.

    Bickel, W. K., Koffarnus, M. N., Moody, L. & Wilson, A. G. The behavioral- and neuro-economic process of temporal discounting: a candidate behavioral marker of addiction. Neuropharmacology 76, 518–527 (2014).

  10. 10.

    Bulley, A. & Pepper, G. V. Cross-country relationships between life expectancy, intertemporal choice and age at first birth. Evol. Hum. Behav. 38, 652–658 (2017).

  11. 11.

    Jarmolowicz, D. P. et al. Robust relation between temporal discounting rates and body mass. Appetite 78, 63–67 (2014).

  12. 12.

    Meier, S. & Sprenger, C. D. Time discounting predicts creditworthiness. Psychol. Sci. 23, 56–58 (2012).

  13. 13.

    Griskevicius, V. et al. When the economy falters, do people spend or save? Responses to resource scarcity depend on childhood environments. Psychol. Sci. 24, 197–205 (2013).

  14. 14.

    Thaler, R. H. Some empirical evidence on dynamic inconsistency. Econ. Lett. 8, 201–207 (1981).

  15. 15.

    Mazur, J. E. in Quantitative analyses of behavior Vol. 5 (eds. Commons, M., et al.). Chapter 3 (Lawrence Erlbaum Associates, 1987).

  16. 16.

    Loewenstein, G. & Prelec, D. Anomalies in intertemporal choice: evidence and an interpretation. Q. J. Econ. 107, 573–597 (1992).

  17. 17.

    Frederick, S., Loewenstein, G. & O’Donoghue, T. Time discounting and time preference: a critical review. J. Econ. Lit. 40, 351–401 (2008).

  18. 18.

    Monterosso, J. R. & Luo, S. An argument against dual valuation system competition: cognitive capacities supporting future orientation mediate rather than compete with visceral motivations. J. Neurosci. Psychol. Econ. 3, 1–14 (2010).

  19. 19.

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

  20. 20.

    Weber, E. et al. Asymmetric discounting in intertemporal choice. Psychol. Sci. 18, 516–523 (2007).

  21. 21.

    Ebert, J. & Prelec, D. The fragility of time: time-insensitivity and valuation of the near and far future. Manag. Sci 53, 1423–1438 (2007).

  22. 22.

    Radu, P. T., Yi, R., Bickel, W. K., Gross, J. J. & McClure, S. M. A mechanism for reducing delay discounting by altering temporal attention. J. Exp. Anal. Behav. 96, 363–385 (2011).

  23. 23.

    Fassbender, C. et al. The decimal effect: behavioral and neural bases for a novel influence on intertemporal choice in healthy individuals and in ADHD. J. Cogn. Neurosci. 26, 2455–2468 (2014).

  24. 24.

    Wulff, D. U. & van den Bos, W. Modeling choices in delay discounting. Psychol. Sci. 29, 1890–1894 (2017).

  25. 25.

    Rodriguez, C. A., Turner, B. M. & McClure, S. M. Intertemporal choice as discounted value accumulation. PLoS One (2014).

  26. 26.

    White, C. N., Ratcliff, R., Vasey, M. W. & McKoon, G. Using diffusion models to understand clinical disorders. J. Math. Psychol. 54, 39–52 (2010).

  27. 27.

    Sullivan, N., Hutcherson, C., Harris, A. & Rangel, A. Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed. Psychol. Sci. 26, 122–134 (2015).

  28. 28.

    van Maanen, L. et al. Neural correlates of trial-to-trial fluctuations in response caution. J. Neurosci. 31, 17488–17495 (2011).

  29. 29.

    Orquin, J. L. & Mueller Loose, S. Attention and choice: a review on eye movements in decision making. Acta Psychol. 144, 190–206 (2013).

  30. 30.

    Krajbich, I., Armel, C. & Rangel, A. Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13, 1292–1298 (2010).

  31. 31.

    Krajbich, I., Lu, D., Camerer, C. & Rangel, A. The attentional drift-diffusion model extends to simple purchasing decisions. Front. Psychol 3, 193 (2012).

  32. 32.

    Konovalov, A. & Krajbich, I. Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning. Nat. Commun. 7, 12438 (2016).

  33. 33.

    Fisher, G. An attentional drift diffusion model over binary-attribute choice. Cognition 168, 34–45 (2017).

  34. 34.

    Glockner, A. & Herbold, A.-K. An eye-tracking study on information processing in risky decision: evidence for compensatory strategies based on automatic processes. J. Behav. Decis. Mak. 24, 71–98 (2011).

  35. 35.

    Franco-Watkins, A. M., Mattson, R. E. & Jackson, M. D. Now or later? Attentional processing and intertemporal choice. J. Behav. Decis. Mak. 29, 206–217 (2016).

  36. 36.

    Venkatraman, V., Payne, J. W. & Huettel, S. A. An overall probability of winning heuristic for complex risky decisions: choice and eye fixation evidence. Organ. Behav. Hum. Decis. Process. 125, 73–87 (2014).

  37. 37.

    Reeck, C., Wall, D. & Johnson, E. J. Search predicts and changes patience in intertemporal choice. Proc. Natl Acad. Sci. USA 114, 11890–11895 (2017).

  38. 38.

    Payne, J. W. Task complexity and contingent processing in decision making: an information search and protocol analysis. Organ. Behav. Hum. Perform. 16, 366–387 (1976).

  39. 39.

    Bruderer Enzler, H., Diekmann, A. & Meyer, R. Subjective discount rates in the general population and their predictive power for energy saving behavior. Energy Policy 65, 524–540 (2014).

  40. 40.

    Chapman, G. B. Temporal discounting and utility for health and money. J. Exp. Psychol. Learn. Mem. Cogn. 22, 771–791 (1996).

  41. 41.

    Tsukayama, E. & Duckworth, A. L. Domain-specific temporal discounting and temptation. Judgm. Decis. Mak. 5, 72–82 (2010).

  42. 42.

    Hardisty, D. J. & Weber, E. U. Discounting future green: money versus the environment. J. Exp. Psychol. Gen. 138, 329–340 (2009).

  43. 43.

    Jimura, K. et al. Domain independence and stability in young and older adults’ discounting of delayed rewards. Behav. Processes 87, 253–259 (2011).

  44. 44.

    Diederich, A. & Oswald, P. Sequential sampling model for multiattribute choice alternatives with random attention time and processing order. Front. Hum. Neurosci 8, 697 (2014).

  45. 45.

    Hoffman, J. E. & Subramaniam, B. The role of visual attention in saccadic eye movements. Percept. Psychophys. 57, 787–795 (1995).

  46. 46.

    Deubel, H. & Schneider, W. X. Saccade target selection and object recognition: evidence for a common attentional mechanism. Vision Res. 36, 1827–1837 (1996).

  47. 47.

    Rehder, B. & Hoffman, A. B. Eyetracking and selective attention in category learning. Cogn. Psychol. 51, 1–41 (2005).

  48. 48.

    Krajbich, I., Hare, T., Bartling, B., Morishima, Y. & Fehr, E. A common mechanism underlying food choice and social decisions. PLoS Comput. Biol. 11, e1004371 (2015).

  49. 49.

    Leong, Y. C., Radulescu, A., Daniel, R., DeWoskin, V. & Niv, Y. Dynamic interaction between reinforcement learning and attention in multidimensional environments. Neuron 93, 451–463 (2017).

  50. 50.

    Schulte-Mecklenbeck, M., Kühberger, A., Gagl, B. & Hutzler, F. Inducing thought processes: bringing process measures and cognitive processes closer together. J. Behav. Decis. Mak. 30, 1001–1013 (2017).

  51. 51.

    Böckenholt, U. & Hynan, L. S. Caveats on a process‐tracing measure and a remedy. J. Behav. Decis. Mak 7, 103–117 (1994).

  52. 52.

    Kwak, Y., Payne, J. W., Cohen, A. & Huettel, S. A. The rational adolescent: strategic information processing during decision making revealed by eye tracking. Cogn. Dev. 36, 20–30 (2015).

  53. 53.

    Venkatraman, V., Payne, J. W., Bettman, J. R., Luce, M. F. & Huettel, S. A. Separate neural mechanisms underlie choices and strategic preferences in risky decision making. Neuron 62, 593–602 (2009).

  54. 54.

    Gigerenzer, G., Czeslinski, J. & Martignon, L. in Decision Science and Technology (eds. Shanteau, J., Mellers, B. & Schum, D.) Chapter 6 (Springer, 1999).

  55. 55.

    Gigerenzer, G. & Gaissmaier, W. Heuristic decision making. Annu. Rev. Psychol. 62, 451–482 (2011).

  56. 56.

    Wittmann, M. & Paulus, M. P. Decision making, impulsivity and time perception. Trends Cogn. Sci. 12, 7–12 (2008).

  57. 57.

    Zhao, C.-X. et al. The hidden opportunity cost of time effect on intertemporal choice. Front. Psychol 6, 311 (2015).

  58. 58.

    Mullett, T. L. & Stewart, N. Implications of visual attention phenomena for models of preferential choice. Decision 3, 231–253 (2016).

  59. 59.

    Laibson, D. Golden eggs and hyperbolic discounting. Q. J. Econ. 112, 443–477 (1997).

  60. 60.

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

  61. 61.

    Andreoni, J., Kuhn, M. A. & Sprenger, C. Measuring time preferences: a comparison of experimental methods. J. Econ. Behav. Organ. 116, 451–464 (2015).

  62. 62.

    Lim, S., Penrod, M. T., Ha, O., Bruce, J. M. & Bruce, A. S. Calorie labeling promotes dietary self-control by shifting the temporal dynamics of health- and taste-attribute integration in overweight individuals. Psychol. Sci. 29, 447–462 (2018).

  63. 63.

    Shimojo, S., Simion, C., Shimojo, E. & Scheier, C. Gaze bias both reflects and influences preference. Nat. Neurosci. 6, 1317–1322 (2003).

  64. 64.

    Tavares, G., Perona, P. & Rangel, A. The attentional drift diffusion model of simple perceptual decision-making. Front. Neurosci 11, 468 (2017).

  65. 65.

    Armel, K. C., Beaumel, A. & Rangel, A. Biasing simple choices by manipulating relative visual attention. Judgm. Decis. Mak. 3, 396–403 (2008).

  66. 66.

    Kunar, M. A., Watson, D. G., Tsetsos, K. & Chater, N. The influence of attention on value integration. Atten. Percept. Psychophys. 79, 1615–1627 (2017).

  67. 67.

    Pärnamets, P. et al. Biasing moral decisions by exploiting the dynamics of eye gaze. Proc. Natl Acad. Sci. USA 112, 4170–4175 (2015).

  68. 68.

    Schkade, D. A. & Kleinmuntz, D. N. Information displays and choice processes: differential effects of organization, form, and sequence. Organ. Behav. Hum. Decis. Process. 57, 319–337 (1994).

  69. 69.

    Kleinmuntz, D. N. & Schkade, D. Information displays and decision processes. Psychol. Sci. 4, 221–227 (1993).

  70. 70.

    Bettman, J. R. & Kakkar, P. Effects of information presentation format on consumer information acquisition strategies. J. Consum. Res. 3, 233–240 (1977).

  71. 71.

    Johnson, E. J., Payne, J. W. & Bettman, J. R. Information displays and preference reversals. Organ. Behav. Hum. Decis. Process. 42, 1–21 (1988).

  72. 72.

    Reutskaja, E., Nagel, R., Camerer, C. F. & Rangel, A. Search dynamics in consumer choice under time pressure: an eye-tracking study. Am. Econ. Rev. 101, 900–926 (2011).

  73. 73.

    Jang, J. M. & Yoon, S. O. The effect of attribute-based and alternative-based processing on consumer choice in context. Mark. Lett. 27, 511–524 (2016).

  74. 74.

    Schkade, D. A. & Johnson, E. J. Cognitive processes in preference reversals. Organ. Behav. Hum. Decis. Process. 44, 203–231 (1989).

  75. 75.

    Lempert, K. M., Glimcher, P. W. & Phelps, E. A. Emotional arousal and discount rate in intertemporal choice are reference dependent. J. Exp. Psychol. Gen. 144, 366–373 (2015).

  76. 76.

    Krajbich, I., Bartling, B., Hare, T. & Fehr, E. Rethinking fast and slow based on a critique of reaction-time reverse inference. Nat. Commun. 6, 1–9 (2015).

  77. 77.

    Bickel, W. K., Yi, R., Landes, R. D., Hill, P. F. & Baxter, C. Remember the future: working memory training decreases delay discounting among stimulant addicts. Biol. Psychiatry 69, 260–265 (2011).

  78. 78.

    Shamosh, N. A. et al. Individual differences in delay discounting: relation to intelligence, working memory, and anterior prefrontal cortex. Psychol. Sci. 19, 904–911 (2008).

  79. 79.

    Bjork, J. M., Momenan, R. & Hommer, D. W. Delay discounting correlates with proportional lateral frontal cortex volumes. Biol. Psychiatry 65, 710–713 (2009).

  80. 80.

    Hare, T. A., Hakimi, S. & Rangel, A. Activity in dlPFC and its effective connectivity to vmPFC are associated with temporal discounting. Front. Neurosci 8, 50 (2014).

  81. 81.

    Lempert, K. M., Speer, M. E., Delgado, M. R. & Phelps, E. A. Positive autobiographical memory retrieval reduces temporal discounting. Soc. Cogn. Affect. Neurosci. 12, 1584–1593 (2017).

  82. 82.

    Hershfield, H. E. Future self-continuity: how conceptions of the future self transform intertemporal choice. Ann. N. Y. Acad. Sci. 1235, 30–43 (2011).

  83. 83.

    Ersner-Hershfield, H., Garton, M. T., Ballard, K., Samanez-Larkin, G. R. & Knutson, B. Don’t stop thinking about tomorrow: individual differences in future self-continuity account for saving. Judgm. Decis. Mak. 4, 280–286 (2009).

  84. 84.

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

  85. 85.

    Zauberman, G., Kim, B. K., Malkoc, S. A. & Bettman, J. R. Discounting time and time discounting: subjective time perception and intertemporal preferences. J. Mark. Res. 46, 543–556 (2009).

  86. 86.

    Read, D., Frederick, S., Orsel, B. & Rahman, J. Four score and seven years from now: the date/delay effect in temporal discounting. Manag. Sci. 51, 1326–1335 (2005).

  87. 87.

    Reppert, T. R., Lempert, K. M., Glimcher, P. W. & Shadmehr, R. Modulation of saccade vigor during value-based decision making. J. Neurosci. 35, 15369–15378 (2015).

  88. 88.

    Coutlee, C. G., Politzer, C. S., Hoyle, R. H. & Huettel, S. A. An abbreviated impulsiveness scale constructed through confirmatory factor analysis of the Barratt Impulsiveness Scale Version 11. Arch. Sci. Psychol. 2, 1–12 (2014).

  89. 89.

    Andreoni, J. & Sprenger, C. Risk preferences are not time preferences. Am. Econ. Rev. 102, 3357–3376 (2012).

  90. 90.

    Loewenstein, G. & Thaler, R. H. Anomalies: intertemporal choice. J. Econ. Perspect. 3, 181–193 (1989).

  91. 91.

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

  92. 92.

    Milosavljevic, M., Malmaud, J. & Huth, A. The drift diffusion model can account for the accuracy and reaction time of value-based choices under high and low time pressure. Judgm. Decis. Mak. 5, 437–449 (2010).

  93. 93.

    Ratcliff, R., Smith, P. L., Brown, S. D. & McKoon, G. Diffusion decision model: current issues and history. Trends Cogn. Sci. 20, 260–281 (2016).

  94. 94.

    Ratcliff, R. & McKoon, G. The diffusion decision model: theory and data for two-choice decision tasks. Neural Comput. 20, 873–922 (2008).

  95. 95.

    Wiecki, T. V., Sofer, I. & Frank, M. J. HDDM: hierarchical Bayesian estimation of the drift-diffusion model in Python. Front. Neuroinform 7, 14 (2013).

  96. 96.

    Srivastava, V., Feng, S. F., Cohen, J. D., Leonard, N. E. & Shenhav, A. A martingale analysis of first passage times of time-dependent Wiener diffusion models. J. Math. Psychol. 77, 94–110 (2017).

  97. 97.

    Busemeyer, J. R. & Diederich, A. Survey of decision field theory. Math. Soc. Sci. 43, 345–370 (2002).

  98. 98.

    MATLAB 2016a. (The MathWorks, Inc., 2016).

  99. 99.

    Wickham, H. ggplot2: Elegant graphics for Data Analysis. (Springer, 2009).

  100. 100.

    R Core Team. R: a Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2017).

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This research was supported by a grant from the National Endowment for Financial Education. D.R.A. was supported by the National Science Foundation Graduate Research Fellowship under grant no. DGE-1644868. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank K.Vo for help with fitting the hyperbolic discounting model. We thank C. Z. Chen and C. Chen for help with data collection. Support for computation came from resources provided by NIH S10-OD-021480.

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  1. Department of Neurobiology, Duke University, Durham, NC, USA

    • Dianna R. Amasino
  2. Center for Cognitive Neuroscience, Duke University, Durham, NC, USA

    • Dianna R. Amasino
    • , Nicolette J. Sullivan
    •  & Scott A. Huettel
  3. Department of Economics, Duke University, Durham, NC, USA

    • Rachel E. Kranton
  4. Department of Psychology and Neuroscience, Duke University, Durham, NC, USA

    • Scott A. Huettel


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D.R.A., R.E.K. and S.A.H. designed the experiment. D.R.A. analysed the data, with input from N.J.S. and S.A.H. N.J.S. provided code for the multi-attribute DDM analyses. D.R.A., N.J.S., R.E.K. and S.A.H. wrote the paper.

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The authors declare no competing interests.

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Correspondence to Scott A. Huettel.

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