Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Comparing experience- and description-based economic preferences across 11 countries

An Author Correction to this article was published on 09 July 2024

This article has been updated

Abstract

Recent evidence indicates that reward value encoding in humans is highly context dependent, leading to suboptimal decisions in some cases, but whether this computational constraint on valuation is a shared feature of human cognition remains unknown. Here we studied the behaviour of n = 561 individuals from 11 countries of markedly different socioeconomic and cultural makeup. Our findings show that context sensitivity was present in all 11 countries. Suboptimal decisions generated by context manipulation were not explained by risk aversion, as estimated through a separate description-based choice task (that is, lotteries) consisting of matched decision offers. Conversely, risk aversion significantly differed across countries. Overall, our findings suggest that context-dependent reward value encoding is a feature of human cognition that remains consistently present across different countries, as opposed to description-based decision-making, which is more permeable to cultural factors.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Behavioural protocol and sample.
Fig. 2: Behavioural results.
Fig. 3: Computational results.

Similar content being viewed by others

Data availability

Data for the present study are available for free (for non-commercial use only) from our OSF.io repository (https://osf.io/yebm9/?view_only=). Source data are provided with this paper.

Code availability

Main analysis scripts are available (for non-commercial use only) from the Human Reinforcement Learning Team GitHub repository (https://github.com/hrl-team/WEIRDbandit).

Change history

References

  1. Ruggeri, K. et al. Replicating patterns of prospect theory for decision under risk. Nat. Hum. Behav. 4, 622–633 (2020).

    Article  PubMed  Google Scholar 

  2. Ruggeri, K. et al. The globalizability of temporal discounting. Nat. Hum. Behav. 6, 1386–1397 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hallsson, B. G., Siebner, H. R. & Hulme, O. J. Fairness, fast and slow: a review of dual process models of fairness. Neurosci. Biobehav. Rev. 89, 49–60 (2018).

    Article  PubMed  Google Scholar 

  4. Kim, B., Sung, Y. S. & McClure, S. M. The neural basis of cultural differences in delay discounting. Phil. Trans. R. Soc. B 367, 650–656 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Rieger, M. O., Wang, M. & Hens, T. Risk preferences around the world. Manag. Sci. 61, 637–648 (2013).

    Article  Google Scholar 

  6. Garcia, B., Cerrotti, F. & Palminteri, S. The description–experience gap: a challenge for the neuroeconomics of decision-making under uncertainty. Phil. Trans. R. Soc. B 376, 20190665 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hertwig, R. & Erev, I. The description–experience gap in risky choice. Trends Cogn. Sci. 13, 517–523 (2009).

    Article  PubMed  Google Scholar 

  8. Wulff, D. U., Mergenthaler-Canseco, M. & Hertwig, R. A meta-analytic review of two modes of learning and the description–experience gap. Psychol. Bull. 144, 140–176 (2018).

    Article  PubMed  Google Scholar 

  9. Niv, Y. Reinforcement learning in the brain. J. Math. Psychol. 53, 139–154 (2009).

    Article  Google Scholar 

  10. Wimmer, G. E., Daw, N. D. & Shohamy, D. Generalization of value in reinforcement learning by humans. Eur. J. Neurosci. 35, 1092–1104 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd edn (MIT Press, 2018).

  12. Frank, M. J., Seeberger, L. C. & O’reilly, R. C. By carrot or by stick: cognitive reinforcement learning in parkinsonism. Science 306, 1940–1943 (2004).

    Article  CAS  PubMed  Google Scholar 

  13. Vandendriessche, H. et al. Contextual influence of reinforcement learning performance of depression: evidence for a negativity bias? Psychol. Med. 53, 4696–4706 (2022).

    Article  PubMed  Google Scholar 

  14. Plonsky, O., Roth, Y. & Erev, I. Underweighting of rare events in social interactions and its implications to the design of voluntary health applications. Judgm. Decis. Mak. 16, 267–289 (2021).

    Article  Google Scholar 

  15. Ho, T. H., Camerer, C. F. & Chong, J.-K. Self-tuning experience weighted attraction learning in games. J. Econ. Theory 133, 177–198 (2007).

    Article  Google Scholar 

  16. Palminteri, S. & Lebreton, M. Context-dependent outcome encoding in human reinforcement learning. Curr. Opin. Behav. Sci. 41, 144–151 (2021).

    Article  Google Scholar 

  17. Palminteri, S. & Lebreton, M. The computational roots of positivity and confirmation biases in reinforcement learning. Trends Cogn. Sci. 26, 607–621 (2022).

    Article  PubMed  Google Scholar 

  18. Kahneman, D. Maps of bounded rationality: psychology for behavioural economics. Am. Econ. Rev. 93, 1449–1475 (2003).

    Article  Google Scholar 

  19. Todd, P. M. & Gigerenzer, G. Bounding rationality to the world. J. Econ. Psychol. 24, 143–165 (2003).

    Article  Google Scholar 

  20. Henrich, J., Heine, S. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010).

    Article  PubMed  Google Scholar 

  21. Palminteri, S. et al. Contextual modulation of value signals in reward and punishment learning. Nat. Commun. 6, 8096 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Bavard, S. et al. Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences. Nat. Commun. 9, 4503 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Klein, T., Ullsperger, M. & Jocham, G. Learning relative values in the striatum induces violations of normative decision making. Nat. Commun. 8, 16033 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hayes, W. M. & Wedell, D. H.Reinforcement learning in and out of context: the effects of attentional focus. J. Exp. Psychol. Learn. Mem. Cogn. 49, 1193–1217 (2023).

    Article  PubMed  Google Scholar 

  25. Juechems, K. & Summerfield, C. Where does value come from? Trends Cogn. Sci. 23, 836–850 (2019).

    Article  PubMed  Google Scholar 

  26. Bavard, S., Rustichini, A. & Palminteri, S. Two sides of the same coin: beneficial and detrimental consequences of range adaptation in human reinforcement learning. Sci. Adv. 7, eabe0340 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hayes, W. M. & Wedell, D. H. Testing models of context-dependent outcome encoding in reinforcement learning. Cognition 230, 105280 (2023).

    Article  PubMed  Google Scholar 

  28. Rustichini, A., Soukupova, M. & Palminteri, S. Adaptive coding is optimal in reinforcement learning. SSRN https://doi.org/10.2139/ssrn.4320894 (2023).

  29. Padoa-Schioppa, C. & Rustichini, A. Rational attention and adaptive coding: a puzzle and a solution. Am. Econ. Rev. 104, 507–513 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Fairhall, A. et al. Efficiency and ambiguity in an adaptive neural code. Nature 412, 787–792 (2001).

    Article  CAS  PubMed  Google Scholar 

  31. Sato, T. et al. An excitatory basis for divisive normalization in visual cortex. Nat. Neurosci. 19, 568–570 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Carandini, M. & Heeger, D. J. Summation and division by neurons in primate visual cortex. Science 264, 1333–1336 (1994).

    Article  CAS  PubMed  Google Scholar 

  33. Freidin, E. & Kacelnik, A. Rational choice, context dependence, and the value of information in European starlings (Sturnus vulgaris). Science 334, 1000–1002 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Pompilio, L. & Kacelnik, A. Context-dependent utility overrides absolute memory as a determinant of choice. Proc. Natl Acad. Sci. USA 107, 508–512 (2010).

    Article  CAS  PubMed  Google Scholar 

  35. Garcia, B. Experiential values are underweighted in decisions involving symbolic options. Nat. Hum. Behav. 7, 611–626 (2023).

    Article  PubMed  Google Scholar 

  36. Gandelman, N. & Hernández-Murillo, R.Risk aversion at the country level. Fed. Res. Bank St. Louis Rev. 97, 53–66 (2015).

    Google Scholar 

  37. Haridon, O. & Vieider, F. All over the map: a worldwide comparison of risk preferences. Quant. Econ. 10, 185–215 (2019).

    Article  Google Scholar 

  38. Juechems, K., Altun, T., Hira, R. & Jarvstad, A. Human value learning and representation reflect rational adaptation to task demands. Nat. Hum. Behav. 6, 1268–1279 (2022).

    Article  PubMed  Google Scholar 

  39. Human Development Report 2020: The Next Frontier: Human Development and the Anthropocene (United Nations Development Programme, 2020).

  40. Muthukrishna, M. et al. Beyond Western, educated, industrial, rich, and democratic (WEIRD) psychology: measuring and mapping scales of cultural and psychological distance. Psychol. Sci. 31, 678–701 (2020).

    Article  PubMed  Google Scholar 

  41. 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).

    Article  PubMed  Google Scholar 

  42. Triandis, H. C. & Gelfland, M. J. Converging measurement of horizontal and vertical individualism and collectivism. J. Pers. Soc. Psychol. 74, 118–128 (1998).

    Article  Google Scholar 

  43. Huber, S. & Huber, O. The centrality of religiosity scale (CRS). Religions 3, 710–724 (2012).

    Article  Google Scholar 

  44. Toplak, M. E., West, R. F. & Stanovich, K. E. Assessing miserly information processing: an expansion of the cognitive reflection test. Think. Reason. 20, 147–168 (2014).

    Article  Google Scholar 

  45. Lichtenstein, S. & Slovic, P. The Construction of Preference (Cambridge Univ. Press, 2006).

  46. Cartwrigth, E. Behavioural Economics (Routledge, 2018).

  47. Alós-Ferrer, C. et al. Preference reversals: time and again. J. Risk Uncertain. 52, 65–97 (2016).

    Article  Google Scholar 

  48. Alós-Ferrer, C. & Granic, G. D. Does choice change preferences? An incentivized test of the mere choice effect. Exp. Econ. 26, 499–521 (2023).

    Article  PubMed  Google Scholar 

  49. Smith, S. Cultural Anthropology (Allyn and Bacon, 1997).

  50. Yates, F. & de Oliveira, S. Culture and decision making. Organ. Behav. Hum. Decis. Process. 136, 106–118 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Choi, I., Choi, J. A. & Norenzayan, A. in Blackwell Handbook of Judgment and Decision Making (eds Koehler, D. J. & Harvey, N.) 504–524 (Blackwell Publishing, 2004).

  52. Gelfand, M. J. et al. Differences between tight and loose cultures: a 33-nation study. Science 332, 1100–1104 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Kitayama, S. & Cohen, D. Handbook of Cultural Psychology 2nd edn (Guilford Press, 2018).

  54. Yates, J. F. et al. Indecisiveness and culture: Incidence, values, and thoroughness. J. Cross Cult. Psychol. 41, 428–444 (2010).

    Article  Google Scholar 

  55. Arkes, H. R., Hirshleifer, D., Jiang, D. & Lim, S. S. A cross-cultural study of reference point adaptation: evidence from China, Korea, and the US. Organ. Behav. Hum. Decis. Process. 112, 99–111 (2010).

    Article  Google Scholar 

  56. Spektor, M. & Seidler, H. Violations of economic rationality due to irrelevant information during learning in decision from experience. Judgm. Decis. Mak. 17, 425–448 (2022).

    Article  Google Scholar 

  57. Barret, H. C.Towards a cognitive science of the human: cross-cultural approaches and their urgency. Trends Cogn. Sci. 24, 620–638 (2020).

    Article  Google Scholar 

  58. Nielsen, M., Haun, D., Kartner, J. & Legare, C. H. The persistent sampling bias in developmental psychology: a call to action. J. Exp. Child Psychol. 162, 31–38 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Linnell, K. J. & Caparos, S. Urbanisation, the arousal system, and covert and overt attentional selection. Curr. Opin. Psychol. 32, 100–104 (2020).

    Article  PubMed  Google Scholar 

  60. Bavard, S. & Palminteri, S. The functional form of value normalization in human reinforcement learning. eLife 12, e83891 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Hayes, W. M. & Wedell, D. Effects of blocked versus interleaved training on relative value learning. Psychon. Bull. Rev. 30, 1895–1907 (2023).

    Article  PubMed  Google Scholar 

  62. Solvi, C. et al. Bumblebees retrieve only the ordinal ranking of foraging options when comparing memories obtained in distinct settings. eLife 11, e78525 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kacelnik, A., Vasconcelos, M. & Monteiro, T. Testing cognitive models of decision-making: selected studies with starlings. Anim. Cogn. 26, 117–127 (2023).

    Article  PubMed  Google Scholar 

  64. Rangel, A. & Clithero, J. A. Value normalization in decision making: theory and evidence. Curr. Opin. Neurobiol. 22, 970–981 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Louie, K. & Glimcher, P. W. Efficient coding and the neural representation of value. Ann. NY Acad. Sci. 1251, 13–32 (2012).

    Article  PubMed  Google Scholar 

  66. McNamara, J. M., Trimmer, P. C. & Houston, A. I. The ecological rationality of state-dependent valuation. Psychol. Rev. 119, 114–119 (2012).

    Article  CAS  PubMed  Google Scholar 

  67. Hunter, L. E. & Daw, N. D. Context-sensitive valuation and learning. Curr. Opin. Behav. Sci. 41, 122–127 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Frey, R., Pedroni, A., Mata, R., Rieskamp, J. & Hertwig, R. Risk preference shares the psychometric structure of major psychological traits. Sci. Adv. 3, e1701381 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Madan, C. R., Ludvig, E. A. & Spetch, M. L. Comparative inspiration: from puzzles with pigeons to novel discoveries with humans in risky choice. Bahav. Process. 160, 10–19 (2019).

    Article  Google Scholar 

  70. Zilker, V. & Pachur, T. Nonlinear probability weighting can reflect attentional biases in sequential sampling. Psychol. Rev. 129, 949–975 (2022).

    Article  PubMed  Google Scholar 

  71. Erev, I. et al. Choice prediction competition: choices from experience and from description. J. Behav. Decis. Mak. 23, 15–47 (2010).

    Article  Google Scholar 

  72. Thaler, R. H. & Sunstein, C. R. Libertarian Paternalism Is Not an Oxymoron Public Law and Legal Theory Working Paper No. 43 (Univ. Chicago, 2003).

  73. Grüne-Yanoff, T., Marchionni, C. & Feufel, M. Toward a framework for selecting behavioural policies: how to choose between boosts and nudges. Econ. Philos. 34, 243–266 (2018).

    Article  Google Scholar 

  74. Brown, P., Cameron, L., Wilkinson, M. & Taylor, D. in The Handbook of Behaviour Change (eds Hagger, M. et al.) 617–631 (Cambridge Univ. Press, 2020).

  75. Gosling, S. D., Rentfrow, P. J. & Swann, W. B. Jr. A very brief measure of the big five personality domains. J. Res. Pers. 37, 504–528 (2003).

    Article  Google Scholar 

  76. Doll, B. B., Jacobs, W. J., Sanfey, A. G. & Frank, M. J. Instructional control of reinforcement learning: a behavioral and neurocomputational investigation. Brain Res. 1299, 74–94 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Li, J., Delgado, M. & Phelps, E. How instructed knowledge modulates the neural systems of reward learning. Proc. Natl Acad. Sci. USA 108, 55–60 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Wang, Z. & Taylor, M. E. Interactive reinforcement learning with dynamic reuse of prior knowledge from human and agent demonstrations. In Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI'19) 3820–3827 (AAAI Press, 2019).

  79. Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  80. R Core Developmemt Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).

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

  82. Rights, J. D. & Sterba, S. K. Quantifying explained variance in multilevel models: an integrative framework for defining R-squared measures. Psychol. Methods 24, 309–338 (2019).

    Article  PubMed  Google Scholar 

  83. Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).

    Article  Google Scholar 

  84. Wagenmakers, E. J. & Farrell, S. AIC model selection using Akaike weights. Psychonom. Bull. Rev. 11, 192–196 (2004).

    Article  Google Scholar 

Download references

Acknowledgements

We thank a number of colleagues and peers, including the members of the Human Reinforcement Learning laboratory and all of the senior researchers who provided feedback during the multiple conference presentations in which this work was featured. We also thank Waseda University and the École Normale Supérieure Department of Cognitive Studies for aiding us with the many logistical obstacles that we had to overcome in order to kickstart this study during the thick of the COVID-19 pandemic. We especially thank all of the participants who kindly contributed their time to make this study a reality. S.P. is supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (RaReMem: 101043804), the Agence Nationale de la Recherche (CogFinAgent: ANR-21-CE23-0002-02; RELATIVE: ANR-21-CE37-0008-01; RANGE: ANR-21-CE28-0024-01) and the Alexander von Humboldt-Stiftung. O.Z., D.K. and A.S. were supported by the Basic Research Program at the National Research University Higher School of Economics (HSE University). U.H. and M.C. were supported by the Israel Science Foundation (1532/20). K.W. was supported by JSPS KAKENHI (22H00090) and JST Moonshot Research and Development (JPMJMS2012). A.B.K., M.G. and D.B. were supported by the National Institute on Drug Abuse (R01DA053282 and R01DA054201 to A.B.K.). J.N. was supported by the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition—Scholar Award (#220020334) and by a Sponsored Research Agreement between Meta and Fundación Universidad Torcuato Di Tella (#INB2376941). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

H.A. is the lead author and researcher responsible for study design, coordination and management between teams, data management and collection and analysis, visualization and writing of the paper. S.P. was the main senior supervisor, who worked hand in hand with H.A. on every aspect of this work, including collaboration management, design, hypothesis development, supervision of the analysis, interpretation of the results, visualization and writing. S.B. was the main author behind the original design that this study replicated and contributed greatly to ensuring that our design indeed reproduced theirs. F.B., D.B., F.C., M.C., M.G., E.J.G., D.K., M.K., G.L., M.S., J.Y and O.Z. reviewed and supported the design of the experiment and its hypotheses. They also took charge of translation and deployment of the experiment in each of their countries, collected data locally and revised the paper. B.B., J.S.C., U.H., A.B.K., J.L., C.O., J.N., G.R., A.S.-J., A.S., B.S. and K.W. are senior supervisors who monitored the study locally, providing insight on the experimental design and commentary on the final version of the paper. In addition, K.W. provided essential scientific and logistical support in deploying the experiment worldwide.

Corresponding authors

Correspondence to Hernán Anlló or Stefano Palminteri.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Thomas J. Faulkenberry and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–13, discussion, analyses and Tables 1–16.

Reporting Summary

Peer Review File

Source data

Source Data Figs. 1–3

HDI score, cultural distance from India and cultural distance from the United States (per country). Value-maximizing choice ratio per decision context per country. Mean nu parameter value, per country, for each condition.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anlló, H., Bavard, S., Benmarrakchi, F. et al. Comparing experience- and description-based economic preferences across 11 countries. Nat Hum Behav 8, 1554–1567 (2024). https://doi.org/10.1038/s41562-024-01894-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-024-01894-9

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing