Skip to main content
SpringerLink
Log in

Cognitive processes underlying distributional preferences: a response time study

  • Original Paper
  • Published:
Experimental Economics Aims and scope Submit manuscript

Abstract

There is ample evidence that people differ considerably in their preferences. We identify individual heterogeneity in type and strength of social preferences in a series of binary three-person dictator games. Based on this identification, we analyze response times in another series of games to investigate the cognitive processes of distributional preferences. We find that response time increases with the number of conflicts between individually relevant motives and decreases with the utility difference between choice options. The selfish motive is more intuitive for subjects who are more selfish. Our findings indicate that the sequential sampling process and the intuition of selfishness jointly produce distribution decisions, and provide an explanation for the mixed results on the correlations between response time and prosociality. Our results also show that it is important to take heterogeneity of preferences into account when investigating the cognitive processes of social decision making.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Supplementary Material A lists the 64 games.

  2. Finite mixture models have a long history and a comprehensive description of the models can be found in McLachlan et al. (2019).

  3. Due to the definitions of differences, there exists a linear relationship between DiffFS-α, DiffFS-β and DiffEfficiency, that is, DiffEfficiency = 2(DiffFS-β − DiffFS-α).

  4. We conducted the finite mixture analysis using the R package flexmix (Grün and Leisch 2008).

  5. In Model I, all subjects can be classified into their types with probabilities of greater than 0.93, and 93.3% of all subjects are classified into their types with probabilities of greater than 0.99. In Model II, only one subject is classified into her type with the probability of less than 0.90 (0.895), and 91.4% of all subjects are classified into their types with probabilities of greater than 0.99. In Model III, only one subject is classified into her type with the probability of less than 0.90 (0.88), and 92.4% of all subjects are classified into their types with probabilities of greater than 0.99. In Model IV, two subjects are classified into their types with probabilities of less than 0.90 (0.79 and 0.86), and 95.2% of all subjects are classified into their types with probabilities of greater than 0.99.

  6. The regression results of Model I, Model II, and Model III are shown in Table A2.

  7. One exception is the coefficient of SignEnvy for subjects of the first norm type in Model I. Envy is inequality aversion toward the person with the highest income. If people care about efficiency, they might like situations that envious people dislike. The relevant motives for each norm type in Model I, Model II, and Model III are shown in Table A3. Apart from some minor differences, all four models identify almost identical norm types.

  8. The posterior probabilities for Model I, Model II, and Model III are shown in Fig. A1 in Supplementary Material A.

  9. The evolution and the distributions of RTs in the experiment are shown in “The evolution of mean response time” and “The distribution of response times in the second-party decisions” in Supplementaery Material B.

  10. To test the robustness of our results, we also conducted the analysis using untransformed RT, which essentially leads to the same results.

  11. Theoretically, it is difficult to put these incorrect decisions into conflict decisions or consistent decisions. On one hand, in these incorrect decisions, all the motives point to the same option, and in this sense, they can be considered to be consistent situations (decisions). On the other hand, the incorrect decisions can also be considered as conflict decisions since choices for these situations are against all the motives. Regardless of whether we consider these decisions to be conflict or consistent decisions, it does not affect the main results.

  12. Here we separate selfishness from the other motives when calculating the utility. We also run additional analyses in which we take both the selfish motive and social motives into account, and the results are similar. In particular, utility difference is a significant explanatory variable for response time.

  13. We also use random samples to test the correlation between RT and the utility difference at the individual level. Specifically, we randomly select half of the data for 100 times, and then we use the selected data to predict the correlation in the other half of the data in each of the 100 times. The results show that there are 73 subjects who always have a positive correlation in all the 100 times, which is different from the chance level of 50% (two-sided binomial test, p < 10-4).

  14. Regression (1) in Table 7 is the regression results for the odd trials in conflict decisions. The regression results for the even trials in conflict decisions are shown in regression (1) in Table C3.

  15. We also checked whether subjects’ selfish behavior is consistent over the rounds. The results show that the strength of selfishness (i.e., the frequency of choosing the selfish option in conflict decisions) in the first half rounds is not different from that in the second half rounds (Wilcoxon signed-rank test, p = 0.303).

  16. We also calculate the strength of selfishness using the odd trials and study the RT in the even trials of conflict decisions. The results are similar and shown in Supplementary Material C.

  17. The results are shown in Supplementary Material D.

References

  • Achtziger, A., & Alós-Ferrer, C. (2014). Fast or rational? A response-times study of Bayesian updating. Management Science,60(4), 923–938.

    Google Scholar 

  • Alós-Ferrer, C., & Strack, F. (2014). From dual processes to multiple selves: Implications for economic behavior. Journal of Economic Psychology,41, 1–11.

    Google Scholar 

  • Andreoni, J., & Miller, J. (2002). Giving according to GARP: An experimental test of the consistency of preferences for altruism. Econometrica,70(2), 737–753.

    Google Scholar 

  • Bear, A., & Rand, D. G. (2016). Intuition, deliberation, and the evolution of cooperation. Proceedings of the National Academy of Sciences,113(4), 936–941.

    Google Scholar 

  • Bolton, G. E., & Ockenfels, A. (2000). ERC: A theory of equity, reciprocity, and competition. American Economic Review,90(1), 166–193.

    Google Scholar 

  • Breitmoser, Y. (2013). Estimation of social preferences in generalized dictator games. Economics Letters,121(2), 192–197.

    Google Scholar 

  • Brocas, I., & Carrillo, J. D. (2014). Dual-process theories of decision-making: A selective survey. Journal of Economic Psychology,41, 45–54.

    Google Scholar 

  • Bruhin, A., Fehr-Duda, H., & Epper, T. (2010). Risk and rationality: Uncovering heterogeneity in probability distortion. Econometrica,78(4), 1375–1412.

    Google Scholar 

  • Cappelen, A. W., Nielsen, U. H., Tungodden, B., Tyran, J.-R., & Wengström, E. (2016). Fairness is intuitive. Experimental Economics,19(4), 727–740.

    Google Scholar 

  • Cappelletti, D., Güth, W., & Ploner, M. (2011). Being of two minds: Ultimatum offers under cognitive constraints. Journal of Economic Psychology,32(6), 940–950.

    Google Scholar 

  • Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification,13(2), 195–212.

    Google Scholar 

  • Chaiken, S., & Trope, Y. (1999). Dual-process theories in social psychology. New York: Guilford Press.

    Google Scholar 

  • Charness, G., & Rabin, M. (2002). Understanding social preferences with simple tests. Quarterly Journal of Economics,117(3), 817–869.

    Google Scholar 

  • Chen, F., & Krajbich, I. (2018). Biased sequential sampling underlies the effects of time pressure and delay in social decision making. Nature Communications,9(1), 3557.

    Google Scholar 

  • Cone, J., & Rand, D. G. (2014). Time pressure increases cooperation in competitively framed social dilemmas. PLoS ONE,9(12), e115756.

    Google Scholar 

  • Cornelissen, G., Dewitte, S., & Warlop, L. (2011). Are social value orientations expressed automatically? Decision making in the dictator game. Personality and Social Psychology Bulletin,37(8), 1080–1090.

    Google Scholar 

  • Dickhaut, J., Smith, V., Xin, B., & Rustichini, A. (2013). Human economic choice as costly information processing. Journal of Economic Behavior & Organization,94, 206–221.

    Google Scholar 

  • Duffy, S., & Smith, J. (2014). Cognitive load in the multi-player prisoner’s dilemma game: Are there brains in games? Journal of Behavioral and Experimental Economics,51, 47–56.

    Google Scholar 

  • Dufwenberg, M., & Kirchsteiger, G. (2004). A theory of sequential reciprocity. Games and Economic Behavior,47(2), 268–298.

    Google Scholar 

  • Engelmann, D., & Strobel, M. (2004). Inequality aversion, efficiency, and maximin preferences in simple distribution experiments. American Economic Review,94(4), 857–869.

    Google Scholar 

  • Erlei, M. (2008). Heterogeneous social preferences. Journal of Economic Behavior & Organization,65(3–4), 436–457.

    Google Scholar 

  • Evans, A. M., Dillon, K. D., & Rand, D. G. (2015). Fast but not intuitive, slow but not reflective: Decision conflict drives reaction times in social dilemmas. Journal of Experimental Psychology: General,144(5), 951–966.

    Google Scholar 

  • Falk, A., & Fischbacher, U. (2006). A theory of reciprocity. Games and Economic Behavior,54(2), 293–315.

    Google Scholar 

  • Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. Quarterly Journal of Economics,114(3), 817–868.

    Google Scholar 

  • Fiedler, S., Glöckner, A., Nicklisch, A., & Dickert, S. (2013). Social value orientation and information search in social dilemmas: An eye-tracking analysis. Organizational Behavior and Human Decision Processes,120(2), 272–284.

    Google Scholar 

  • Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics,10(2), 171–178.

    Google Scholar 

  • Fisman, R., Kariv, S., & Markovits, D. (2007). Individual preferences for giving. American Economic Review,97(5), 1858–1876.

    Google Scholar 

  • Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives,19(4), 25–42.

    Google Scholar 

  • Frydman, C., & Nave, G. (2016). Extrapolative beliefs in perceptual and economic decisions: Evidence of a common mechanism. Management Science,63(7), 2340–2352.

    Google Scholar 

  • Fudenberg, D., & Levine, D. K. (2006). A dual-self model of impulse control. American Economic Review,96(5), 1449–1476.

    Google Scholar 

  • Greiner, B. (2015). Subject pool recruitment procedures: Organizing experiments with ORSEE. Journal of the Economic Science Association,1(1), 114–125.

    Google Scholar 

  • Grün, B., & Leisch, F. (2008). FlexMix Version 2: Finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software,28(4), 1–35.

    Google Scholar 

  • Hauge, K. E., Brekke, K. A., Johansson, L.-O., Johansson-Stenman, O., & Svedsäter, H. (2016). Keeping others in our mind or in our heart? Distribution games under cognitive load. Experimental Economics,19(3), 562–576.

    Google Scholar 

  • Houser, D., Keane, M., & McCabe, K. (2004). Behavior in a dynamic decision problem: An analysis of experimental evidence using a Bayesian type classification algorithm. Econometrica,72(3), 781–822.

    Google Scholar 

  • Hutcherson, C. A., Bushong, B., & Rangel, A. (2015). A neurocomputational model of altruistic choice and its implications. Neuron,87(2), 451–462.

    Google Scholar 

  • Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist,58(9), 697–720.

    Google Scholar 

  • Kahneman, D. (2011). Thinking, fast and slow. New York: Macmillan.

    Google Scholar 

  • Kerschbamer, R. (2015). The geometry of distributional preferences and a non-parametric identification approach: The equality equivalence test. European Economic Review,76, 85–103.

    Google Scholar 

  • Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience,13(10), 1292–1298.

    Google Scholar 

  • Krajbich, I., Bartling, B., Hare, T., & Fehr, E. (2015a). Rethinking fast and slow based on a critique of reaction-time reverse inference. Nature Communications,6, 7455.

    Google Scholar 

  • Krajbich, I., Hare, T., Bartling, B., Morishima, Y., & Fehr, E. (2015b). A common mechanism underlying food choice and social decisions. PLoS Computational Biology,11(10), e1004371.

    Google Scholar 

  • Krajbich, I., Oud, B., & Fehr, E. (2014). Benefits of neuroeconomic modeling: New policy interventions and predictors of preference. American Economic Review,104(5), 501–506.

    Google Scholar 

  • Lohse, J., Goeschl, T., & Diederich, J. H. (2017). Giving is a question of time: Response times and contributions to an environmental public good. Environmental & Resource Economics,67(3), 455–477.

    Google Scholar 

  • Lotito, G., Migheli, M., & Ortona, G. (2013). Is cooperation instinctive? Evidence from the response times in a public goods game. Journal of Bioeconomics,15(2), 123–133.

    Google Scholar 

  • McLachlan, G. J., & Basford, K. E. (1988). Mixture models: Inference and applications to clustering (Vol. 84). New York: M. Dekker.

    Google Scholar 

  • McLachlan, G. J., & Jones, P. N. (1988). Fitting mixture models to grouped and truncated data via the EM algorithm. Biometrics, 571–578.

  • McLachlan, G. J., Lee, S. X., & Rathnayake, S. I. (2019). Finite mixture models. Annual Review of Statistics and Its Application,6, 355–378.

    Google Scholar 

  • Merkel, A. L., & Lohse, J. (2019). Is fairness intuitive? An experiment accounting for subjective utility differences under time pressure. Experimental Economics,22(1), 24–50.

    Google Scholar 

  • Murphy, R. O., Ackermann, K. A., & Handgraaf, M. J. (2011). Measuring social value orientation. Judgment and Decision Making,6(8), 771–781.

    Google Scholar 

  • Nielsen, U. H., Tyran, J.-R., & Wengström, E. (2014). Second thoughts on free riding. Economics Letters,122(2), 136–139.

    Google Scholar 

  • Peysakhovich, A., & Rand, D. G. (2016). Habits of virtue: Creating norms of cooperation and defection in the laboratory. Management Science,62(3), 631–647.

    Google Scholar 

  • Piovesan, M., & Wengström, E. (2009). Fast or fair? A study of response times. Economics Letters,105(2), 193–196.

    Google Scholar 

  • Rabin, M. (1993). Incorporating fairness into game theory and economics. American Economic Review,83(5), 1281–1302.

    Google Scholar 

  • Rand, D. G., Brescoll, V., Everett, J. A., Capraro, V., & Barcelo, H. (2016). Social heuristics and social roles: Intuition favors altruism for women but not for men. Journal of Experimental Psychology: General,145(4), 389–396.

    Google Scholar 

  • Rand, D. G., Greene, J. D., & Nowak, M. A. (2012). Spontaneous giving and calculated greed. Nature,489(7416), 427–430.

    Google Scholar 

  • Rand, D. G., Peysakhovich, A., Kraft-Todd, G. T., Newman, G. E., Wurzbacher, O., Nowak, M. A., et al. (2014). Social heuristics shape intuitive cooperation. Nature Communications,5, 3677.

    Google Scholar 

  • Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review,85(2), 59–108.

    Google Scholar 

  • Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review,111(2), 333–367.

    Google Scholar 

  • Rubinstein, A. (2007). Instinctive and cognitive reasoning: A study of response times. The Economic Journal,117(523), 1243–1259.

    Google Scholar 

  • Schulz, J. F., Fischbacher, U., Thöni, C., & Utikal, V. (2014). Affect and fairness: Dictator games under cognitive load. Journal of Economic Psychology,41, 77–87.

    Google Scholar 

  • Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin,119(1), 3–22.

    Google Scholar 

  • Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review,8(3), 220–247.

    Google Scholar 

  • Tinghög, G., Andersson, D., Bonn, C., Böttiger, H., Josephson, C., Lundgren, G., et al. (2013). Intuition and cooperation reconsidered. Nature,498(7452), E1–E2.

    Google Scholar 

  • Verkoeijen, P. P., & Bouwmeester, S. (2014). Does intuition cause cooperation? PLoS ONE,9(5), e96654.

    Google Scholar 

Download references

Acknowledgements

We would like to thank Carlos Alós-Ferrer, Dominik Bauer, Colin Camerer, David Dohmen, Fabian Dvorak, Gerald Eisenkopf, Ernst Fehr, David Grammling, Jan Hausfeld, Konstantin Hesler, Ian Krajbich, Daniel Martin, Gideon Nave, David Rand, Katrin Schmelz, Simeon Schudy, Roberto Weber and Irenaeus Wolf as well as participants of 2014 ESA European Meeting in Prague, 2014 Zurich Workshop on Experimental and Behavioral Economic Research, the Second International Meeting on Experimental and Behavioral Social Sciences in Toulouse for helpful comments and discussions. Fadong Chen gratefully acknowledges support from the National Natural Science Foundation of China (Grant No. 71803174), the Qiantang River Talents Program, and the Fundamental Research Funds for the Central Universities in China. Urs Fischbacher gratefully acknowledges support from the German Research Foundation (DFG) through research unit FOR 1882 “Psychoeconomics”.

Author information

Authors and Affiliations

  1. School of Management, Zhejiang University, Hangzhou, 310058, China

    Fadong Chen

  2. Neuromanagement Lab, Zhejiang University, Hangzhou, 310058, China

    Fadong Chen

  3. Department of Economics, University of Konstanz, Universitätsstr. 10, 78457, Konstanz, Germany

    Urs Fischbacher

  4. Thurgau Institute of Economics, Hauptstr. 90, 8280, Kreuzlingen, Switzerland

    Urs Fischbacher

Authors
  1. Fadong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Urs Fischbacher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Urs Fischbacher.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 468 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, F., Fischbacher, U. Cognitive processes underlying distributional preferences: a response time study. Exp Econ 23, 421–446 (2020). https://doi.org/10.1007/s10683-019-09618-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10683-019-09618-x

Keywords

JEL Classification

Search

Navigation