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
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
27,99 € / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
118,99 € per year
only 9,92 € per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout



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
09 July 2024
A Correction to this paper has been published: https://doi.org/10.1038/s41562-024-01946-0
References
Ruggeri, K. et al. Replicating patterns of prospect theory for decision under risk. Nat. Hum. Behav. 4, 622–633 (2020).
Ruggeri, K. et al. The globalizability of temporal discounting. Nat. Hum. Behav. 6, 1386–1397 (2022).
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).
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).
Rieger, M. O., Wang, M. & Hens, T. Risk preferences around the world. Manag. Sci. 61, 637–648 (2013).
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).
Hertwig, R. & Erev, I. The description–experience gap in risky choice. Trends Cogn. Sci. 13, 517–523 (2009).
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).
Niv, Y. Reinforcement learning in the brain. J. Math. Psychol. 53, 139–154 (2009).
Wimmer, G. E., Daw, N. D. & Shohamy, D. Generalization of value in reinforcement learning by humans. Eur. J. Neurosci. 35, 1092–1104 (2012).
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd edn (MIT Press, 2018).
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).
Vandendriessche, H. et al. Contextual influence of reinforcement learning performance of depression: evidence for a negativity bias? Psychol. Med. 53, 4696–4706 (2022).
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).
Ho, T. H., Camerer, C. F. & Chong, J.-K. Self-tuning experience weighted attraction learning in games. J. Econ. Theory 133, 177–198 (2007).
Palminteri, S. & Lebreton, M. Context-dependent outcome encoding in human reinforcement learning. Curr. Opin. Behav. Sci. 41, 144–151 (2021).
Palminteri, S. & Lebreton, M. The computational roots of positivity and confirmation biases in reinforcement learning. Trends Cogn. Sci. 26, 607–621 (2022).
Kahneman, D. Maps of bounded rationality: psychology for behavioural economics. Am. Econ. Rev. 93, 1449–1475 (2003).
Todd, P. M. & Gigerenzer, G. Bounding rationality to the world. J. Econ. Psychol. 24, 143–165 (2003).
Henrich, J., Heine, S. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010).
Palminteri, S. et al. Contextual modulation of value signals in reward and punishment learning. Nat. Commun. 6, 8096 (2015).
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).
Klein, T., Ullsperger, M. & Jocham, G. Learning relative values in the striatum induces violations of normative decision making. Nat. Commun. 8, 16033 (2017).
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).
Juechems, K. & Summerfield, C. Where does value come from? Trends Cogn. Sci. 23, 836–850 (2019).
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).
Hayes, W. M. & Wedell, D. H. Testing models of context-dependent outcome encoding in reinforcement learning. Cognition 230, 105280 (2023).
Rustichini, A., Soukupova, M. & Palminteri, S. Adaptive coding is optimal in reinforcement learning. SSRN https://doi.org/10.2139/ssrn.4320894 (2023).
Padoa-Schioppa, C. & Rustichini, A. Rational attention and adaptive coding: a puzzle and a solution. Am. Econ. Rev. 104, 507–513 (2014).
Fairhall, A. et al. Efficiency and ambiguity in an adaptive neural code. Nature 412, 787–792 (2001).
Sato, T. et al. An excitatory basis for divisive normalization in visual cortex. Nat. Neurosci. 19, 568–570 (2016).
Carandini, M. & Heeger, D. J. Summation and division by neurons in primate visual cortex. Science 264, 1333–1336 (1994).
Freidin, E. & Kacelnik, A. Rational choice, context dependence, and the value of information in European starlings (Sturnus vulgaris). Science 334, 1000–1002 (2011).
Pompilio, L. & Kacelnik, A. Context-dependent utility overrides absolute memory as a determinant of choice. Proc. Natl Acad. Sci. USA 107, 508–512 (2010).
Garcia, B. Experiential values are underweighted in decisions involving symbolic options. Nat. Hum. Behav. 7, 611–626 (2023).
Gandelman, N. & Hernández-Murillo, R.Risk aversion at the country level. Fed. Res. Bank St. Louis Rev. 97, 53–66 (2015).
Haridon, O. & Vieider, F. All over the map: a worldwide comparison of risk preferences. Quant. Econ. 10, 185–215 (2019).
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).
Human Development Report 2020: The Next Frontier: Human Development and the Anthropocene (United Nations Development Programme, 2020).
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).
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).
Triandis, H. C. & Gelfland, M. J. Converging measurement of horizontal and vertical individualism and collectivism. J. Pers. Soc. Psychol. 74, 118–128 (1998).
Huber, S. & Huber, O. The centrality of religiosity scale (CRS). Religions 3, 710–724 (2012).
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).
Lichtenstein, S. & Slovic, P. The Construction of Preference (Cambridge Univ. Press, 2006).
Cartwrigth, E. Behavioural Economics (Routledge, 2018).
Alós-Ferrer, C. et al. Preference reversals: time and again. J. Risk Uncertain. 52, 65–97 (2016).
Alós-Ferrer, C. & Granic, G. D. Does choice change preferences? An incentivized test of the mere choice effect. Exp. Econ. 26, 499–521 (2023).
Smith, S. Cultural Anthropology (Allyn and Bacon, 1997).
Yates, F. & de Oliveira, S. Culture and decision making. Organ. Behav. Hum. Decis. Process. 136, 106–118 (2016).
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).
Gelfand, M. J. et al. Differences between tight and loose cultures: a 33-nation study. Science 332, 1100–1104 (2011).
Kitayama, S. & Cohen, D. Handbook of Cultural Psychology 2nd edn (Guilford Press, 2018).
Yates, J. F. et al. Indecisiveness and culture: Incidence, values, and thoroughness. J. Cross Cult. Psychol. 41, 428–444 (2010).
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).
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).
Barret, H. C.Towards a cognitive science of the human: cross-cultural approaches and their urgency. Trends Cogn. Sci. 24, 620–638 (2020).
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).
Linnell, K. J. & Caparos, S. Urbanisation, the arousal system, and covert and overt attentional selection. Curr. Opin. Psychol. 32, 100–104 (2020).
Bavard, S. & Palminteri, S. The functional form of value normalization in human reinforcement learning. eLife 12, e83891 (2023).
Hayes, W. M. & Wedell, D. Effects of blocked versus interleaved training on relative value learning. Psychon. Bull. Rev. 30, 1895–1907 (2023).
Solvi, C. et al. Bumblebees retrieve only the ordinal ranking of foraging options when comparing memories obtained in distinct settings. eLife 11, e78525 (2022).
Kacelnik, A., Vasconcelos, M. & Monteiro, T. Testing cognitive models of decision-making: selected studies with starlings. Anim. Cogn. 26, 117–127 (2023).
Rangel, A. & Clithero, J. A. Value normalization in decision making: theory and evidence. Curr. Opin. Neurobiol. 22, 970–981 (2012).
Louie, K. & Glimcher, P. W. Efficient coding and the neural representation of value. Ann. NY Acad. Sci. 1251, 13–32 (2012).
McNamara, J. M., Trimmer, P. C. & Houston, A. I. The ecological rationality of state-dependent valuation. Psychol. Rev. 119, 114–119 (2012).
Hunter, L. E. & Daw, N. D. Context-sensitive valuation and learning. Curr. Opin. Behav. Sci. 41, 122–127 (2021).
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).
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).
Zilker, V. & Pachur, T. Nonlinear probability weighting can reflect attentional biases in sequential sampling. Psychol. Rev. 129, 949–975 (2022).
Erev, I. et al. Choice prediction competition: choices from experience and from description. J. Behav. Decis. Mak. 23, 15–47 (2010).
Thaler, R. H. & Sunstein, C. R. Libertarian Paternalism Is Not an Oxymoron Public Law and Legal Theory Working Paper No. 43 (Univ. Chicago, 2003).
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).
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).
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).
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).
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).
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).
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
R Core Developmemt Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).
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).
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).
Wagenmakers, E. J. & Farrell, S. AIC model selection using Akaike weights. Psychonom. Bull. Rev. 11, 192–196 (2004).
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
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
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.
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.
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41562-024-01894-9