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LETTER doi:10.3.038/nature11467 Spontaneous giving and calculated greed David G. Randt•2-', Joshua D. Greene2+ & Martin A. Nowakm•ss Cooperation is central to human social behaviour". However, We recruited 212 subjects from around the world using the online choosing to cooperate requires individuals to incur a personal cost labour market Amazon Mechanical Turk (AMT)". AMT provides a to benefit others. Here we explore the cognitive basis of cooperative reliable subject pool that is more diverse than a typical sample of decision-making in humans using a dual-process framework"-". college undergraduates (see Supplementary Information, section 1). We ask whether people are predisposed towards selfishness, behav- In accordance with standard AMT wages, each subject was given ing cooperatively only through active self-control; or whether they USS0.40 and was asked to choose how much to contribute to a are intuitively cooperative, with reflection and prospective reason- common pool. Any money contributed was doubled and split evenly ing favouring 'rational' self-Interest. To investigate this issue, we among the four group members (see Supplementary Information, perform ten studies using economic games. We find that across a section 3, for experimental details). range of experimental designs, subjects who reach their decisions Figure la shows the fraction of the endowment contributed in the more quiddy are more cooperative. Furthermore, forcing subjects slower half of decisions compared to the faster half. Faster decisions to decide quickly increases contributions, whereas instructing result in substantially higher contributions compared with slower them to reflect and forcing them to decide slowly decreases con- decisions (rank sum test, P= 0.007). Furthermore, as shown in tributions. Finally, an induction that primes subjects to trust their Fig. lb, we see a consistent decrease in contribution amount with intuitions increases contributions compared with an induction that promotes greater reflection. To explain these results, we propose that cooperation is intuitive because cooperative heuristics are developed a 75% in daily life where cooperation is typically advantageous. We then validate predictions generated by this proposed mechanism. Our 65% - I results provide convergent evidence that intuition supports coopera- tion in social dilemmas, and that reflection can undermine these cooperative Impulses. I 55% - I Many people are willing to make sacrifices for the common good". 8 Here we explore the cognitive mechanisms underlying this cooperative 0 45% - behaviour. We use a dual-process framework in which intuition and reflection interact to produce decisions'°-".". Intuition is often associated with parallel processing, automaticity, effortlessness, lack 35% of insight into the decision process and emotional influence. Reflection Faster decisions Sower decisions 1-10s >10s is often associated with serial processing, effortfulness and the rejection of emotional influence"-1".". In addition, one of the psychological features most widely used to distinguish intuition from b 100% reflection is processing speed: intuitive responses are relatively fast, 80% 4 whereas reflective responses require additional time for deliberation". 56 Here we focus our attention on this particular dimension, which is 60% closely related to the distinction between automatic and controlled 4 processing". 40% Viewing cooperation from a dual-process perspective raises the 12 I6 following questions are we intuitively self-interested, and is it only 20% through reflection that we reject our selfish impulses and force ourselves to cooperate? Or are we intuitively cooperative, with 0% reflection upon the logic of self-interest causing us to rein in our 02 0.6 1 1.4 1.8 2.2 cooperative urges and instead act selfishly? Or, alternatively, is there Decision tine (loa,,N) no cognitive conflict between intuition and reflection? Here we address these questions using economic cooperation games. Figure 1 I Faster decisions are more cooperative Subjects who reach their We begin by examining subjects' decision times. The hypothesis decisions more quickly contribute more in a one-shot PCG (n = 212). This that self-interest is intuitive, with prosociality requiring reflection to suggests that the intuitive response is to be cooperative. a, Using a median split override one's selfish impulses, predicts that faster decisions will be less on decision time, we compare the contribution levels of the faster half versus slower half of decisions. The average contribution is substantially higher for the cooperative. Conversely, the hypothesis that intuition preferentially faster decisions. b, Plotting contribution as a function of 168w- transformed supports prosocial behaviour, whereas reflection leads to increased decision time shows a negative relationship between decision time and selfishness, predicts that faster decisions will be more cooperative. contribution. Dot size is proportional to the number of observations, listed next As a first test of these competing hypotheses, we conducted a one- to each dot. Error bars, mean = s.e.m. (see Supplementary Information, shot public goods game" (PGG) with groups of four participants. sections 2 and 3, for statistical analysis and further details). 'Program kw Evolutionary Dynarnics.Karrard Univers4y.Cambridge.Massachusells02138.USA.2Deperlma4 ol Psycholori.HanrardUniversty.Combdidge.Abssachuselts02138.USA.30eparlmentor Psychdogy.YaleUroversily.NewHaren.Connecleul 0652Q USA 'Deportment ollAathanalics. Harvard LInnerstly.Carribridge.Massachuselts02138.USODepartnenl ol Organismic and Evolutionary Bool3gy. Harvard Unwersrty.Cambndge.i.lassechusetts0213a USA •Theseauthen ccobibuled equally to the work SEPTEMBER 2012 I VOL 489 I NATURE I 4 2 7 02012 Macmillan Publishers Limited. All rights reserved EFTA01146514  RESEARCH LETTER increasing decision time (Tobit regression, coefficient = -15.84, a 75% P=0.019; see Supplementary Information, sections 2 and 3, for statistical details). These findings suggest that intuitive responses are 65% more cooperative. Next we examined data from all of our previously published social I dilemma experiments for which decision time data were recorder-22. 55% I In these studies, conducted in the physical laboratory with college S students, the experimental software automatically recorded decision 45% times,but these data had not been previously analysed. To examine the psychology that subjects bring with them into the laboratory, we 35% focused on play in the first round of each experimental session. In a Time pressure Unconstrained Time delay one-shot prisoner's dilemma (ri = 48)20, a repeated prisoner's dilemma Constraint condition with execution errors (it = 278)31, a repeated prisoner's dilemma with and without costly punishment (pi = 104)", and a repeated PGG with b 75% Contribution and without reward and/or punishment (n = 192)7, we find the same negative relationship between decision time and cooperation (see 65%- Prediction of Supplementary Information, section 4, for details). These results show others' contribution the robustness of our decision-time findings: across a range of experi- mental designs, and with students in the physical laboratory as well as 55% - with an international online sample, faster decisions are associated S O with more prosociality. We now demonstrate the causal link between intuition and coop- 45% I eration suggested by these correlational studies. To do so, we recruited 35% another 680 subjects on AMT and experimentally manipulated their Time pressure Time delay decision times in the same one-shot PGG used above. In the 'time Constraint condition pressure' condition, subjects were forced to reach their decision quickly (within 10 s). Subjects in this conditionhave less time to reflect O 75% than in a standard PGG, and therefore their decisions are expected to be more intuitive. In the 'time delay' condition, subjects were 65% - instructed to carefully consider their decision and forced to wait for at least lOs before choosing a contribution amount. Thus, in this 55% - OS condition, decisions are expected to be driven more by reflection (see Supplementary Information, section 5, for experimental details). The results (Fig. 2a) are consistent with the correlational observa- 45% - tions in Fig 1. Subjects in the time-pressure condition contribute sig- nificantly more money on average than subjects in the time-delay 35% condition (rank sum, P< 0.001). Moreover, we find that both manip- Promote intuition or Promote reflection or ulation conditions differ from the average behaviour in the baseline inhibit reflection inhibit intuition experiment in Fig. 1, and in the expected directions: subjects under Priming conckhon time-pressure contribute more than unconstrained subjects (rank Figure 2 I Inducing intuitive thinking promotes cooperation. a. Forcing sum, P= 0.058), whereas subjects who are instructed to reflect and subjects to decide quickly (10 s or less) results in higher contributions, whereas delay their decision contribute less than unconstrained subjects (rank forcing subjects to decide slowly (more than 10 s) decreases contributions sum, P = 0.028), although the former difference is only marginally (n = 680). This demonstrates the causal link between decision time and significant. See Supplementary Information, section 5, for regression cooperation suggested by the correlation shown in Fig. 1. b, We replicate the analyses. finding that forcing subjects to decide quickly promotes cooperation in a second Additionally, we recruited 211 Boston-area college students and study run in the physical laboratory with tenfold larger stakes = 211). We also replicated our time-constraint experiment in the physical laboratory find that the time constraint has no significant effect on subjects' predictions with tenfold higher stakes (Fig. 2b). We find again that subjects in the concerning the average contributions of other group members. Thus, the manipulation acts through preferences rather than beliefs. c. Priming intuition time-pressure condition contribute significantly more money than (or inhibiting reflection) increases cooperation relative to priming reflection (or subjects in the time-delay condition (rank sum, P= 0.032). We also inhibiting intuition) (n = 343). 'This finding prosides further evidence for the assessed subjects' expectations about the behaviour of others in their specific role ofintuition versus reflection inmotivating cooperation. as suggested group, and find no significant difference across conditions (rank sum, by the decision time studies. Error bars, mean ± s.c.m. (see Supplementary P= 0.360). Thus, subjects forced to respond more intuitively seem to Information, sections 5-7, for statistical analysis and further details). have more prosocial preferences, rather than simply contributing more because they are more optimistic about the behaviour of others (see Supplementary Information, section 6, for experimental details direction, or careful reasoning had led them in the right direction. and analysis). Consistent with the seven experiments described above, we find that We next used a conceptual priming manipulation that explicitly contributions are significantly higher when subjects are primed to invokes intuition and reflection'. We recruited 343 subjects on promote intuition relative to reflection (Fig. 2c; rank sum, P= 0.011; AMT to participate in a one-shot PGG experiment. The first condition see Supplementary Information, section 8, for experimental details promotes intuition relative to reflection: before reading the PGG and analysis). instructions, subjects were assigned to write a paragraph about a situ- These results therefore raise the question of why people are ation in which either their intuitionhad led them in the right direction, intuitively predisposed towards cooperation. We propose the follow- or careful reasoning had led them in the wrong direction. Conversely, ing mechanism: people develop their intuitions in the context of daily the second condition promotes reflection: subjects were asked to write life, where cooperation is typically advantageous because many about either a situation in which intuition had led them in the wrong important interactions are repeated""22, reputation is often at 428 1 NATURE 1 VOL 489 20 SEPTEMBER 201: 02012 Macmillan Publishers Limited. All rights reserved EFTA01146515  LETTER RESEARCH stake's" and sanctions for good or bad behaviour might exist9b associated with higher contributions (Fig. 3b; see Supplementary Thus, our subjects develop cooperative intuitions for social Information, section 10, for experimental details and statistical interactions and bring these cooperative intuitions with them into analysis). the laboratory. As a result, their automatic first response is to be Thus, there are some people for whom the intuitive response is more cooperative. It then requires reflection to overcome this cooperative cooperative and the reflective response is less cooperative; and there impulse and instead adapt to the unusual situation created in these are other people for whom both the intuitive and reflective responses experiments, in which cooperation is not advantageous. lead to relatively little cooperation. But we find no cases in which the This hypothesis makes clear predictions about individual difference intuitive response is reliably less cooperative than the reflective res- moderators of the effect of intuition on cooperation, two of which we ponse. As a result, on average, intuition promotes cooperation relative now test. First, if the effects described above result from intuitions to reflection in our experiments. formed through ordinary experience, then greater familiarity with By showing that people do not have a single consistent set of social laboratory cooperation experiments should attenuate these effects. preferences, our results highlight the need for more cognitively com- We test this prediction on AMT with a replication of our conceptual plex economic and evolutionary models of cooperation, along the lines priming experiment. As predicted, we fmd a significant interaction of recent models for non-social decision-making'ra'-16. Furthermore, between prime and experience it is only among subjects naive to the our results suggest a special role for intuition in promoting coopera- experimental task that promoting intuition increases cooperation tion". For further discussion, and a discussion of previous work (Fig. 3a; see Supplementary Information, section 9, for experimental exploring behaviour in economic games from a dual-process perspec- details and statistical analysis). tive, see Supplementary Information, sections 12 and 13. This mechanism also predicts that subjects will only fmd coopera- On the basis of our results, it may be tempting to conclude that tion intuitive if they developed their intuitions in daily-life settings in cooperation is 'innate' and genetically hardwired, rather than the which cooperation was advantageous. Even in the presence of repe- product of cultural transmission. This is not necessarily the case: tition, reputation and sanctions, cooperation will only be favoured if intuitive responses could also be shaped by cultural evolution" and enough other people are similarly cooperative". We tested this pre- social learning over the course of development. However, our results diction on AMT with a replication of our baseline correlational study. are consistent with work demonstrating spontaneous helping As predicted, it is only among subjects that report having mainly behaviour in young children". Exploring the role of intuition and cooperative daily-life interaction partners that faster decisions are reflection in cooperation among children, as well as cross-culturally, can shed further light on this issue. a ■ Primed to promote intuition Here we have explored the cognitive underpinnings of cooperation 75% in humans. Our results help to explain the origins of cooperative • Primed to promote reflection behaviour, and have implications for the design of institutions that 65% aim to promote cooperation. Encouraging decision-makers to be I _I_ maximally rational may have the unintended side-effect of making 55% them more selfish. Furthermore, rational arguments about the import- ance of cooperating may paradoxically have a similar effect, whereas 45% interventions targeting prosocial intuitions may be more successful30. Exploring the implications of our findings, both for scientific under- standing and public policy, is an important direction for future study: 35% Naive Experienced although the cold logic of self-interest is seductive, our first impulse is to cooperate. Previous experience with experimental setting METHODS SUMMARY b ■ Faster dectsicns Across studies 1.6, 8,9 and 10,a total of 1.955 subjects were recruited using AMR'" 75%. to participate in one of a series ofvariations on the one-shot PGG, played through ■ Slower decisions an online survey website. Subjects received $030 for participating. and could earn up to SI more based on the PGG. In the PGG, subject were given S0A0 and chose 65% - how much to contribute to a 'common project'. All contributions were doubled and split equally among four group members. Once all subjects in the experiment I 55%. had made their decisions, groups of four were randomly matched and the resulting ■ payoffs were calculated. Each subject was then paid accordingly through the AMT payment system. and was informed about the average contribution of the other 45% - members of his or her group. No deception was used. Instudy 7, a total of 21I subjects were recruited from the Boston. Massachusetts. 35% metropolitan area through the Harvard University Computer Laboratory for Cooperative Uncooperative Experiment Research subject pool to participate in an experiment at the Opinion of daily-life interaction partners Harvard Decision Science Laboratory. Participation was restricted to students under 35 years of age. Subjects received a $5 show-up fee for arriving on time Figure 3 I Evidence that cooperative intuitions from daily lift spill over into and had the opportunity to earn up to an additional S12 in the experiment. the laboratory. Two experiments validate predictions of our hypothesis that Subjects played a single one-shot PGG through the same website interface used subjects develop their cooperative intuitions in the context ofdaily life, in which in the AMT studies, but with tenfold larger stakes (maximum earnings of 510). cooperation is advantageous. a, Priming that promotes reliance on intuition Subjects were then asked to predict the average contribution of their other group increases cooperation relative to priming promoting reflection,but only among numbers and had the chance to win up to an additional S2 based on their accuracy. naive subjects that report no previous experience with the experimental setting These experiments were approved by the Harvard University Committee on the where cooperation is disadvantageous (ra = 256). b, Faster decisions arc Use of Human Subjects in Research. associated with higher contribution levels, but only among subjects who report For further details of the experimental meihods.set Supplementary Information. having cooperative daily-life interaction partners = 341). As in Fig. la, a median split is carried out on decision times, separating decisions into the faster Received 13 December 2011; accepted 2 August 2012. versus slower half. Error bars, mean ± s.e.m. (see Supplementary Information• sections 9 and 10. for statistical analysis and further details). 1. Trivers. R. The evolution of reciprocal altruism. Q. Rev. Biol. 46, 35-57 (1971) 12 SEPTEMBER 2012 I VOL 499 I NATURE 1 429 02012 Macmillan Publishers Limited. All rights reserved EFTA01146516  RESEARCH LETTER 2. Fudenberg.D.& Maskin. E. The folk theorem in repeated games with discounting 22. Dreher. A, Rand, D.C.. Fudenberg.D.& Nowak M.A Winners don't punish. Nature or with incomplete information. Economefrica 54, 533-554 (1986} 452, 348-351(2008). 3. Nowak M. A.& Sigmund. K Evolution of indirect reciprocity. Nature 437, 23. Shenhay.A.. Rand. D. G. & Greene. J. D. Divine intuition: ccgnitive style influences 1291-1298(2005} belief in God. J. Exp. PsychoL Gen. 143.423-428 (2012). 4. Boyd. R. Gintis. H.. Bowles. S.& Richerson. P. J. The evolution of altruistic 24. Benhabib.J. & Bisin, A. Modeling internal commitment mechanisms and self- punishment. Proc. Nat Acad. Sci. USA 100, 3531-3535 (2003). control: a neuroeconomics approach to consumption-saving decisions. Games 5. Milinski.M..Semrrenn. D.& KrambeckH.J. Reputation helps solve the 'tragedy of Econ. BEAN. 52.460-492 (2005). the commons. Nature 415.424-426 (2002). 25. Fudenberg.D.& Levine. D. KA Dual-self model of impulse confrci.Am. Econ. Rev. 6. Rockenbach. & Milinsld. M. The efficient interaction of indirect reciprocity and 96, 1449-1476 (2C06} costly punishment. Nature 444, 718-723 (2006). 2& McClure, S. M. Laibson. D.I, Loewenstein. G. & Cohen J. D. Separate neural 7. Rand, D.G.. Dreher, A.. Ellirgsen.T, Fudenberg. a& Nowak. M.A Positive systems value immediate and delayed monetary rewards. Science 306, 503-507 interactions promote public cooperation. Science 325, 1272-1275 (2009). (2004). & Fehr, E& Gachter.S. Altruistic punishment in humans. Nature 415,137-140 27. Bowies S. & Gintis H. in The Economyas a EvolvingComplex System 3 (eds Blume, (2002). L and Durlaut S. N.) 339-364 (2002} 9. Rand, D. G..Arbesman. & & Christakis N. A. Dynamic social networks promote 26 Richerson. P.J. & Boyd, R. Not byGenes Alone: How Culture TransformedHuman cooperation in experiments with humans. Proc Nay Aced Sci USA 108, Evolution. (Univ. Chicago Press 2005). 19193-19198 (2011). 29. Wameken, F. & Tomasello. M. Altnistic helping in human infants and young la Sloman, S.A. The empirkal case for two systems of reasonirg.Psychol Bull 119, chimpanzees. Science 311, 1301-1303 (2006). 3-22 (1996). 30. Bowies S.Policies designed for seff-interested citizens mayundermine "the moral 11. Stanovich. K. E& West. R. F. Individual differences in rational thougbt. J. Exp. sentiments": evidence from economic experiments. Science 320, 1605-1609 Psychal 127, 161-188(1998} f2003). 12 Chaiken.S.&Trope.Y. Dual-ProcessTheories in SocialPsycholog/(Gu Word, 1999} 13. Kahneman, D. A perspective on judgment and choice: mapping bounded Supplementary Information is available in the online version of the paper. rationality. Am. Psycho). 58, 697-720 (2003). 14. Plessner. Betsch. C. & Betsch. T. Intuition in Judgment and Decision Making Acknowledgements We thank H. Ahlblad, O. Amir. F. Fu. O. Hauser.). Horton and (Lawrence Erlbaum. 2008} R Kane for assistance with carrying out the experiments. and P. Blake. S. BmvIes. N. Christakis. F. Cushman. A. Dreher. T. Ellingsen. F. Fu. D. Fudenberg. 0. Hauser. 15. Kahneman, D. Thinking Fast and Sfotv (Straus and Giroux. 2011} 16. Shiffrin.R_ M.& Schneider. W.Controlled and automatic information processing: II. J. Jordan. M. Johannesson, M. Manapat J. Paxton. A Peysakhovkh. A Shenhay. J. Sirlin-Rand. M. van Veelen and 0. Wurzbacher for discussion and comments This Perceptual teaming, automatic attending, and a general theory. Psycho,. Rev. 84, 127-190(1977} work was supported in part bya National Science Foundation grant (SES-082197/3 to 17. Milkw.E K&Ccben J. D.An integrative theory of prefrontal cortex function.Annu. JD.G.). D.G.R and MAN. are supported by grants from the John Templeton Foundation. Rev. Neumsci. 24,167-202 (2001). 18. Frederick S,Ccgnitive reflection and decision making.J.Econ.Parmect 19.25-42 Author Contributions D.G.R..J.D.G. and MAN. designed the experiments D.G.R (2005). carried out the experiments and statistical analyses.ancl D.G.R.JD.G.and MAN. wrote 19. Horton. J. J., Rand, D.G. & Zeckhauser, R. J. The online laboratory: conducting the paper. experiments in a real labor market. Erp. Econ. 14, 399-425 (2011). 20. Pfeiffer. T.. Tran, L Krumme. C.& Rand. D. G. The value of reputation.J. R. Soc. Author Information Reprints and permissions information is available at Interface http://dxdoiorg/10.1098/rsif2012.0332 (20 June 2012). bwnvnature.comireprints. The authors declare no competing financial interests 21. Fudenberg. D.. Rand, a G.& Dreber.A Slow to anger and fast to forgive: Readers are welcome to comment on the online version of the paper. Correspondence cooperation in an uncertain world. Am. Econ. Rev. 102, 720-749 (2012} and requests for materials should be addressed to D.G.R.([email protected]). 430 I NATURE I VOL 489 I 20 SEPTEMBER 2012 02012 Macmillan Publishers Limited. All nghts reserved EFTA01146517 SUPPLEMENTARY INFORMATION doi:10.1038/nature11467 1. Online recruitment procedure using Amazon Mechanical Turk 2 2. Log-transforming decision times 3 3. Study 1: Correlational decision time experiment on AMT 4 4. Studies 2 - 5: Reanalysis of previously published experiments run in the physical laboratory 6 5. Study 6: Time pressure / time delay experiment on AMT 12 6. Study 7: Time pressure / time delay experiment with belief elicitation in the physical laboratory 14 7. Behavior on AMT versus the physical laboratory (Study 6 vs Study 7) 17 8. Study 8: Conceptual priming experiment on AMT 18 9. Study 9: Conceptual priming experiment with experience measure and decision times on AMT 22 10. Study 10: Correlational experiment on AMT with moderators, individual differences in cognitive style, and additional controls 26 12. Implications for economic and evolutionary models 36 13. Previous dual-process research using economic games 37 14. Supplemental study: Experiment on AMT showing that detailed comprehension questions induce reflective thinking and reduce cooperation 38 15. Experimental instructions 40 References 47 WWW.NATURE-COM/NATUREI I EFTA01146518 doi:10.1038/nature11467 RESEARCH SUPPLEMENTARY INFORMATION 1. Online recruitment procedure using Amazon Mechanical Turk Subjects for many of the experiments in this paper were recruited using the online labor market Amazon Mechanical Turk (AMT)". AMT is an online labor market in which employers can employ workers to complete short tasks (generally less than 10 minutes) for relatively small amounts of money (generally less than $1). Workers receive a baseline payment and can be paid an additional bonus depending on their performance. This makes it easy to run incentivized experiments: the baseline payment is a `show-up fee,' and the bonus payment is determined by the points earned in the experiment. One major advantage of AMT is it allows experimenters to easily expand beyond the college student convenience samples typical of most economic game experiments. Among American subjects, AMT subjects have been shown to be significantly more nationally representative than college student sampled/. Furthermore, workers on AMT are from all around the world: in our experiments, 37% of the subjects lived outside of the United States, with more than half of the non-American subjects living in India. In our statistical analyses below, we show that there is no significant difference in the effects we are studying between US and non-US subjects. This diversity of subject pool participants is particularly helpful in the present study, given our focus on intuitive motivations that may vary based on life experience. Of course, issues exist when running experiments online that do not exist in the traditional laboratory. Running experiments online necessarily involves some loss of control, since the workers cannot be directly monitored as in the traditional lab; hence, experimenters cannot be certain that each observation is the result of a single person (as opposed to multiple people making joint decisions at the same computer), or that one person does not participate multiple times (although AMT goes to great lengths to try to prevent this, and we use filtering based on IP address to further reduce repeat play). Moreover, although the sample of subjects in AMT experiments is more diverse than samples using college undergraduates, we are obviously restricted to people that participate in online labor markets. To address these potential concerns, recent studies have explored the validity of data gathered using AMT (for an overview, see ref I). Most pertinent to our study are two quantitative direct replications using economic games. The first shows quantitative agreement in contribution behavior in a repeated public goods game between experiments conducted in the physical lab and those conducted using AMT with approximately 10-fold lower stakes2. The second replication again found quantitative agreement between the lab and AMT with 10-fold lower stakes, this time in cooperation in a one-shot Prisoner's Dilemmas. The latter study also conducted a survey on the extent to which subjects trust that they will be paid as described in the instructions (a critical element for economic game experiments) and found that AMT subjects were only slightly less trusting than subjects from a physical laboratory subject pool at Harvard University (trust of 5.4 vs 5.7 on a 7-point Likert scale). A third study compared behavior on AMT in games using $1 stakes with unincentivized games, examining the public goods game, the dictator game, the ultimatum game and the trust games. Consistent with previous research in the physical laboratory, adding stakes was only found to affect play in the dictator game, where subjects were significantly more generous in the unincentivized dictator game compared to the $1 dictator game. Furthermore, the average behavior in these games on AMT was within the range of WWW.NATURE.CONVNATURg 12 EFTA01146519 doi:10.1038/nature11467 RESEARCH SUPPLEMENTARY INFORMATION averages reported from laboratory studies, demonstrating further quantitative agreement between AMT and the physical lab. In additional studies, it has also been shown that AMT subjects display a level of test-retest reliability similar to what is seen in the traditional lab on measures of political beliefs, self- esteem, Social Dominance Orientation, and Big-Five personality traits'', as well as belief in God, age, gender, education level and incomeI•6; and do not differ significantly from college undergraduates in terms of attentiveness or basic numeracy skills, as well as demonstrating similar effect sizes as undergraduates in tasks examining framing effects, the conjunction fallacy, and outcome bias7. The present studies add another piece of evidence for the validity of experiments run on AMT by comparing our AMT studies with decision time data from previous laboratory experiments (Main text Figure 2): Both online and in the lab, subjects that take longer to make their decisions are less cooperative. 2. Log-transforming decision limes In several of our experiments, we predict cooperation as a function of decision times. However, the distribution of decision times (measured in seconds) is heavily right-skewed, as we did not impose a maximum decision time (decision times for the baseline decision time experiment, Study 1, are shown in Figure S la). Thus linear regression is not appropriate using non- transformed decision times, as the few decision times that are extremely large exert undue influence on the fit of the regression. To address this issue, we log10-transform decision times in all analyses (log10 transformed decision times for the baseline decision time experiment are shown in Figure S lb). As reported below, our main results are qualitatively similar if we instead analyze non-transformed decision times and exclude outliers (subjects with decision times more than 3 standard deviations above the mean decision time). a b 4,2 O L IL LL O O 100 200 300 1.5 2.5 DeOeRN mme0ecaay WDOOSIOITim? jscenIsil Figure SI. (a) Distribution of decision times in the baseline experiment. (b) Distribution of log10 transformed decision times in the baseline experiment. WWW.NATURE-COWNATURII3 EFTA01146520 doi:10.1038/nature11467 RESEARCH SUPPLEMENTARY INFORMATION 3. Study 1: Correlational decision time experiment on AMT Methods In the baseline experiment (main text Figure I), subjects were recruited using AMT and told they would receive a $0.50 show-up fee for participating, and would have the chance to earn up to an additional $1.00 based on the outcome of the experiment. After accepting the task, subjects were redirected to website where they participated in the study. First subjects were shown the Instructions Screen, where they read a set of instructions describing the following one-shot public goods game: Players interacted in groups of 4; each player received 40 cents; players chose how many cents to contribute to the group (in increments of 2 to avoid fractional cent amounts) and how many to keep; all contributions were doubled and split equally by all group members. After they were finished reading the instructions, subjects clicked OK and were taken to the Contribution Screen. Here they entered their contribution decision and clicked OK. The website software recorded how long it took each subject to make her decision (in seconds), that is, the amount of time she spent on the Contribution Screen. Time spent on the Instructions Screen did not count towards our decision time measure. (Time spent on the Instructions Screen is examined below in Study 10 and shown not to influence cooperation.) After entering their contribution amount, subjects were taken to the Comprehension Screen in which they answered two comprehension questions to determine whether they understood the payoff structure: "What level of contribution earns the highest payoff for the group as a whole?" (correct answer = 40) and "What level of contribution earns the highest payoff for you personally?" (correct answer = 0). Subjects were then taken to a demographic questionnaire and given a completion code. We included comprehension questions after the contribution decision, rather than before as is typical in most laboratory experiments, because we were concerned about the possibility of pushing all of our subjects into a reflective mindset prior to their decision-making. (In SI Section 14, we discuss a supplemental experiment that validates this concern by demonstrating that subjects who complete comprehension questions, including a detailed payoff calculation, before making their decision choose to contribute significantly less than those who complete the comprehension questions afterward). Importantly, we show that our result is robust to controlling for comprehension, indicating that the negative relationship between decision time and cooperation is not driven by a lack of comprehension among the faster responders. Once the decisions of all subjects had been collected, subjects were randomly matched into groups of 4, payoffs were calculated, and bonuses were paid through AMT. Payoffs were determined exactly as described in the instructions, and no deception was used. WWW.NATURe.COM/NATUREI EFTA01146521 doi:10.1038/nature11467 RESEARCH SUPPLEMENTARY INFORMATION Results We begin with descriptive statistics: N=212 Mean Std Contribution 23.83 15.39 Decision time 15.92 22.96 Log I 0(Decision time) 1.03 0.34 Age 28.02 8.73 Gender (0=M, I=F) 0.42 0.49 US Residency (0=N, I=Y) 0.45 0.49 Failed Comprehension (0=N, 1=Y) 0.28 0.45 In the baseline experiment, we ask how the amount of time a subject takes to make her contribution decision relates to the amount contributed. To do so, we perform a set of Tobit regressions with robust standard errors, taking contribution amount as the dependent variable (Table SI). Tobit regression allows us to account for the fact that contribution amounts were censored at 0 and 40 (the minimum and maximum contribution amounts). In the first regression, we take log-10 transformed decision time as the independent variable, and find a significant negative relationship. In the second regression, we show that this effect remains significant when including controls for age, gender, US residency, and failing to correctly answering the comprehension questions, as well as dummies for education level. In the third regression, we show that this effect also remains significant when excluding extreme decision times for which there was comparatively little data (regression 3 includes only subjects with 0.6 < log10(decision time) < 1.2). We also continue to find a significant negative relationship between decision time and contribution (coeff=-0.497, p=0.018) using non-transformed decision times and excluding outliers (subjects with decision times more than 3 standard deviations above the mean [mean decision time = 15.9, std = 23.0 implies a cutoff of 85 seconds]) and including controls for age, gender, US residency and comprehension. It is worthwhile to note that the average level of contribution (59.6% of the endowment) of our subjects recruited from AMT is well within the range of average contribution levels observed in previous studies. Our PGG uses a marginal per capita return (MPCR) on public good investment of 0.5 (for every cent contributed, each player earns 0.5 cents). We used an MPCR of 0.5, rather than the value of 0.4 used in many previous studies (where contributions are multiplied by 1.6 and split