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The Collective Benefits of Feeling Good and Letting Go: Positive Emotion and (dis)Inhibition Interact to Predict Cooperative Behavior

  • David G. Rand ,

    Affiliations: Department of Psychology, Yale University, New Haven, Connecticut, United States of America, Department of Economics, Yale University, New Haven, Connecticut, United States of America, School of Management, Yale University, New Haven, Connecticut, United States of America

  • Gordon Kraft-Todd,

    Affiliation: Department of Psychology, Yale University, New Haven, Connecticut, United States of America

  • June Gruber

    Affiliation: Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado, United States of America

The Collective Benefits of Feeling Good and Letting Go: Positive Emotion and (dis)Inhibition Interact to Predict Cooperative Behavior

  • David G. Rand, 
  • Gordon Kraft-Todd, 
  • June Gruber


Cooperation is central to human existence, forming the bedrock of everyday social relationships and larger societal structures. Thus, understanding the psychological underpinnings of cooperation is of both scientific and practical importance. Recent work using a dual-process framework suggests that intuitive processing can promote cooperation while deliberative processing can undermine it. Here we add to this line of research by more specifically identifying deliberative and intuitive processes that affect cooperation. To do so, we applied automated text analysis using the Linguistic Inquiry and Word Count (LIWC) software to investigate the association between behavior in one-shot anonymous economic cooperation games and the presence inhibition (a deliberative process) and positive emotion (an intuitive process) in free-response narratives written after (Study 1, N = 4,218) or during (Study 2, N = 236) the decision-making process. Consistent with previous results, across both studies inhibition predicted reduced cooperation while positive emotion predicted increased cooperation (even when controlling for negative emotion). Importantly, there was a significant interaction between positive emotion and inhibition, such that the most cooperative individuals had high positive emotion and low inhibition. This suggests that inhibition (i.e., reflective or deliberative processing) may undermine cooperative behavior by suppressing the prosocial effects of positive emotion.


Cooperation plays an integral role in our lives, sustaining friendships and business relationships and laying the foundation for successful organizations and nations [118]. When people cooperate, they can achieve more than each could working alone: cooperation creates benefit, and is positively non-zero sum. Yet cooperation often requires individuals to bear personal costs in order to create those benefits, creating a social dilemma where individual and collective interests are in conflict. Given the societal benefits of cooperation, understanding the psychological underpinnings of cooperative behavior is of both scientific and practical importance [1933]. Thus, it is critical to identify potential processes that may influence cooperative behavior.

A growing literature uses economic games to explore the cognitive underpinnings of cooperation from a dual process perspective. In these experiments, participants choose between keeping money for themselves or spending money to benefit others (cf. [34, 35]). For example, consider the following Public Goods Game (PGG): each member of a group of four starts with a $10 endowment, and decides how much to keep versus contribute to a “common project”. All contributions are then doubled and split evenly among the four group members. Contributing is individually costly regardless of the actions of the other group members (each dollar contributed is doubled and split four ways, so for every dollar you contribute you get back only $0.50). Yet if everyone contributes, everyone doubles their money. To understand the cognitive underpinnings of cooperation in such games, a dual process perspective is often employed, whereby decisions are conceptualized as resulting from the competition between two cognitive systems: one that is fast, automatic, intuitive and often emotional, and another that is slow, controlled, and deliberative [3642]. Turning this dual process lens to cooperation raises the following question: Are people by default selfish, only acting cooperatively through inhibition and self-control? Or do we have automatic (perhaps emotional) impulses to cooperate, which are undermined by selfish deliberation?

A series of recent experiments support the latter possibility; namely, that time pressure [4347], cognitive load [4850], conceptual priming of intuition [44, 51], deciding about present rather than future allocations [52, 53], and disruption of the right lateral prefrontal cortex [54] can increase participants’ willing to pay money in economic games to benefit others (although some studies also find null effects [5558]). Further evidence comes from studies find that participants project cooperative frames onto neutrally framed economic games [59]; that behavior in one-shot games is influenced by previous play of long versus short repeated games, but only for participants who rely on heuristics [60]; the priming intuition increases charitable donations to identifiable but not statistical victims [61]; that people with low self-control are more likely to sacrifice to benefit their romantic partners [62]; and that people who risk their lives to save strangers overwhelmingly describe their decision-processes as automatic and intuitive [63].

Yet the particular elements of deliberation and intuition that affect cooperative behavior remain unclear. Here we examine this issue. To guide our investigation, we turn to the Social Heuristics Hypothesis (SHH), which has recently been proposed as a mechanistic explanation of the above findings [43]. The SHH adds an explicitly dual-process perspective to theories of cultural evolution and norm internalization [6467], and suggests that strategies which are typically successful in daily social life get automatized as social intuitions. When in atypical settings (such as one-shot interactions without future consequences), deliberation can then over-ride these automatic responses in favor of responses which are better tailored to the task at hand (selfishness, in the context of one-shot interactions).

Based on this model, a clear candidate for a specific deliberative process undermining cooperation is inhibition. Because daily life interactions typically involve future consequences [4, 10] many people acquire cooperative defaults, which are then inhibited by deliberation when people find themselves in interaction without future consequences. Therefore, we predict that inhibition will be associated with reduced cooperation.

Turning from deliberative processes to intuitive processes, a promising candidate is emotion, and in particular positive emotion. Emotional influences on prosocial behavior such as cooperation are well-documented, and positive emotion in particular is a critical component for adaptive social functioning and associated with many salutary social effects relevant to cooperation [6870]. For example, a core function of positive emotion is to provide fertile ground to build and maintain vital social resources to function within a larger group structure [71]. For example, positive feelings provide meaning and enjoyment when forming social alliances [7173] and help foster relationship satisfaction and commitment [74, 75], and promote increased prosocial behaviors necessary for cooperation, including helping others [76, 77]. Finally, greater self-reported positive emotion levels have been associated with an individual’s ability to understand others’ emotions, a critical skill in building cooperative ties with others [78, 79]. These beneficial social effects of positive emotion suggest that feeling positive might directly influence the extent to which an individual cooperates with others. Consistent with this, gratitude as a critical positive emotion has been shown to promote cooperation in economic exchanges [80]. Therefore, we predict that positive emotion will be associated with increased cooperation.

Furthermore, we predict an interaction between inhibition and positive emotion. Specifically, if positive emotion is a key component of intuitive processing that favors cooperation, and deliberation impairs cooperation through the reigning-in of positive emotion, then the negative effect of inhibition should be greater when more positive emotion is present (and the positive effect of positive emotion should be reduced when inhibition is present). To test these predictions, we predict behavior in economic cooperation games using the presence of positive emotion and inhibition (quantified using the well-validated Linguistic Inquiry and Word Count tool [81], LIWC; see below for further details) in free-response narratives spontaneously generated after (Study 1, N = 4,218) or during (Study 2, N = 236) the cooperative decision-making process.

Study 1

In Study 1, we investigated the role positive emotion and inhibition play in cooperation by analyzing participants’ free response descriptions of their decision-making process in a one-shot economic cooperation game, written after finishing the game.


Participants were 4,218 adult U.S, residents (44.9% female, Mage 31.0 years [SD = 11.05 yrs], median education level “Attended College”) drawn from nine different social dilemma studies run on Amazon Mechanical Turk [8286] between January 2011 and January 2013 in which free-response narratives were collected but not previously analyzed. These studies were approved by the Yale University Human Subjects Committee IRB Protocol #1307012383. All subjects provided written informed consent prior to participating, and this was approved by the Human Subjects Committee. See S1 Dataset for raw data.

Cooperation Task

Seven studies involved a one-shot Public Goods Game where groups of four participants chose how much of $0.40 to keep and how much to contribute to a common project, with contributions be doubled and split equally among the four group members. The other two involved a one-shot continuous (rather than binary) Prisoner’s Dilemma, which is a two-player analog of the PGG: each subject chose how much of $0.40 to keep and how much to transfer to the other, with transfers being doubled. Each study involved two or more experimental conditions, which we aggregate for the present analyses. These economic games are well established as measures of cooperation [4, 34], and have been used previously to specifically examine the role of intuition versus deliberation in cooperation [4346]. Earnings in all experiments were determined by game play, and no deception was used.

After making their decision in the game, participants’ comprehension of the game payoff structure was assessed by asking: “What level of contribution earns the highest payoff for the group as a whole?” and “What level of contribution earns the highest payoff for you personally?”, and were informed that they had to answer correctly in order to get paid (27.5% of subjects that answered one or both questions incorrectly—most errors involved the incorrect belief that cooperation was individually optimal). Subjects who failed the comprehension check may not have understood that they were facing a social dilemma, and thus it is not clear whether their behavior is actually “cooperative”. Therefore our analyses include a check for whether comprehension interacts with our variables of interest.

Quantitative Analysis of Emotion and Inhibition

At the end of the study, participants provided free-response answers to the prompt “describe why you made your decision in the game” (Length of Response: M = 17.51 words, SD = 11.67), from which we performed a quantitative text analysis using the Linguistic Inquiry and Word Count (LIWC). LIWC is a computerized text analysis program that counts the frequencies of words which have been demonstrated to represent different psychologically relevant categories [81]. Specifically, LIWC scans the words of a text document against its internal dictionary that contains over 70 categories, and assigns each word into a specific category. It then outputs the percentage of words in the document belonging to each category [87]. LIWC has been widely used to track naturally-occurring behavior and language use across a variety of contexts, including classical literature, press conferences, everyday conversations, and personal narratives (e.g. [88, 89]). Moreover, the LIWC allows for automated and efficient coding of features of verbal behavior that may be less detectable to even a highly trained team of human coders. Thus, carefully attending to the specific words people use during naturalistically occurring narratives can be reliably coded using the LIWC.

Of particular relevance for the current work, we derived the presence of words in three theoretically relevant LIWC categories; specifically, we focused on word frequencies related to positive emotion (“positive emotions” category, including words such as “love,” “nice,” and “sweet”), reflective processing (“inhibition” category, including words such as “block,” “constrain,” and “stop”), as well as negative emotion (“negative emotions” category, including words such as “hurt,” “ugly,” and “nasty”) as a control. These three affective and cognitive LIWC categories have been widely used in previous LIWC studies [63, 9092].

To provide further insight into the LIWC classification, sample PGG narratives that received high LIWC scores for each of these three categories are shown in Table 1. We see that texts classified into each category are broadly consistent with the relevant concepts, even if the mapping is not perfect. We follow the procedure of [63], and focus our analysis how the presence of these three categories of words (0 = absent, 1 = present) predicted subsequent cooperation in the game (the distribution of LIWC scores was very right-skewed, making a continuous rather than binary analysis less meaningful).

Table 1. Example texts that received high LIWC scores for positive emotion, negative emotion, and inhibition.


As seen in Fig. 1, participants indicating positive emotion were more cooperation than those who did not mention positive emotion, while those who mentioned inhibition cooperated less than those who did not. Furthermore, the presence of inhibition substantially diluted the effect of positive emotion. An ANOVA predicting cooperation confirmed this visual impression, finding a significant main effect of Positive Emotion [F(1,4214) = 45.02, p<0.001], a significant main effect of Inhibition [F(1,4214) = 56.97, p<0.001], and a significant interaction between Positive Emotion and Inhibition [F(1,4214) = 8.31, p<0.01]. Examining simple effects, we found a significant effect of Positive Emotion in participants both with [F(1,522) = 2.22, p<0.05] and without [F(1,3692) = 14.1, p<0.0001] Inhibition, and a significant negative effect of Inhibition in participants both with [F(1,2473) = 9.08, p<0.0001] and without [F(1,1741) = 2.82, p<0.005] Positive Emotion.

Fig 1. Fraction of endowment spent on cooperation in Study 1.

Error bars indicate standard errors of the mean.

Importantly, these results were robust to controlling for the presence (or absence) of Negative Emotion, as well as age (continuous), gender, education (categorical), and total word count (continuous) (Positive Emotion [F(1,4204) = 39.86, p<0.001], Inhibition [F(1,4204) = 58.91, p<0.001], Positive Emotion x Inhibition interaction [F(1,4204) = 6.76, p<0.01]. The robustness of our results for Positive Emotion when controlling for Negative Emotion suggests that it was truly the presence of positive emotion, rather than the absence of negative emotion, that drove our findings.

We also note a lack of significant interaction between our variables of interest and comprehension of the game payoff structure (Comprehension x Positive Emotion [F(1,4209) = 0.55, p = 0.460], Comprehension x Inhibition [F(1,4209) = 2.22, p = 0.136], Comprehension x Positive Emotion x Inhibition [F(1,4209) = 0.00, p = 0.981]), as well as the fact that our results persist when excluding participants who answered any questions incorrectly (without controls: PE [F(1,3,051) = 40.72, p<0.001], Inhibition [F(1,3,051) = 29.09, p<0.001], Positive Emotion x Inhibition [F(1,3051) = 7.12, p<0.01]; with controls: Positive Emotion [F(1,3,041) = 35.94, p<0.001], Inhibition [F(1,3041) = 31.11, p<0.001], Positive Emotion x Inhibition [F(1,3041) = 5.84, p<0.05]).

Finally, although the effect of Negative Emotion was not the focus of this investigation, for completeness we report results for Negative Emotion separately, adopting a parallel analytic approach as for Positive Emotion. These results did not yield a significant main effect of Negative Emotion [F(1,4214) = 0.51, p = .475], but did yield a significant main effect of Inhibition [F(1,4214) = 22.64, p<0.001], and a significant interaction of Negative Emotion x Inhibition [F(1,4214) = 14.20, p<0.001]; leading to a significant negative simple effect of Negative Emotion without Inhibition [F(1,4214) = 33.59, p<0.001], and a non-significant but trending positive effect of Negative Emotion with Inhibition [F(1,4214) = 3.38, p = 0.066].

Study 2

In Study 2, we further investigated the role positive emotion and inhibition play in cooperation using stream-of-consciousness narratives written during a one-shot PGG, rather than afterwards, as participants’ post-decision recollections of their decision-making process may be biased. These studies were approved by the Yale University Human Subjects Committee IRB Protocol #1307012383. All subjects provided written informed consent prior to participating, and this was approved by the Human Subjects Committee. See S2 Dataset for raw data.


Participants were 236 adult US residents (41.8% female, Mage 30.7 years [SD = 10.64 yrs], median education level “Attended College”) recruited using Amazon Mechanical Turk in January 2013.

Cooperation Task

Participants played the same PGG described in Study 1 (groups of 4, $0.40 endowment, contributions multiplied by 2), but with comprehension assessed before making their decision rather than afterwards (22.0% of participants answered one or both questions incorrectly). To elicit participants’ motivations during their decision, rather than afterwards as in Study 1, they were given the prompt “While you are considering your decision, please type your thoughts as they occur to you” along with a corresponding text box, immediately above a series of radio buttons used to entered their contribution choice. We wanted participants to record enough of their thoughts for us to have sufficient signal for our analyses, but we were also concerned that mandating a minimum length might affect the decision making process. Thus we randomized subjects into conditions with a minimum of 0, 10, 30, or 60 seconds of thinking. This allowed us to generate sufficient signal, while also assessing any potential effects of mandating certain writing time periods.

Quantitative Analysis of Emotion and Inhibition

Participants’ free-response narratives were analyzed using the same LIWC software as in Study 1.


Preliminary analyses found a significant three-way interaction between Positive Emotion, Inhibition and Comprehension failure [F(1,228) = 4.67, p<0.05]. To avoid bias introduced by misunderstanding the game structure, we therefore focused our analysis on the 184 participants that answered the comprehension questions correctly. As seen in Fig. 2, we did not find a main effect for Positive Emotion [F(1,180) = 0.11, p = 0.75] or Inhibition [F(1,180) = 1.20, p = 0.28]. Similarly to Study 1, however, we found a significant Positive Emotion x Inhibition interaction [F(1,180) = 4.05, p<0.05] such that positive emotion increased cooperation, but this effect was suppressed by Inhibition. Examining simple effects, we found a significant effect of Positive Emotion in participants without Inhibition [F(1,148) = 3.14, p<0.005] but not with Inhibition [F(1,32) = 0.93, p = 0.36]; and a significant negative effect of Inhibition in participants with Positive Emotion [F(1,119) = 3.30, p<0.005] but not without Positive Emotion [F(1,61) = 0.51, p = 0.61].

Fig 2. Fraction of endowment spent on cooperation in Study 2.

Error bars indicate standard errors of the mean.

As in Study 1, these results were robust to controlling for the presence of Negative Emotion, as well as age (continuous), gender, education (categorical), total word count of the description (continuous) and minimum writing time (categorical) (Positive Emotion [F(1,162) = 0.05, p = .822], Inhibition [F(1,162) = 1.98, p = 0.161], Positive Emotion x Inhibition [F(1,162) = 4.24, p<0.05]), again indicating that it is truly the presence of positive emotion, rather than the absence of negative emotion, that drives the observed results.

Finally, for completeness we describe parallel analyses for Negative Emotion, which did not find a significant main effect of Negative Emotion [F(1,232) = 0.46, p = .498] or a significant Negative Emotion x Inhibition interaction [F(1,232) = 2.36, p = 0.126], but did find a significant main effect of Inhibition [F(1,232) = 7.56, p<0.001].


Across two studies, we provided evidence that positive emotion motivates cooperation, that inhibition undermines cooperation, and that these two processes interact: positive emotion without inhibition was associated with the highest level of cooperation. This interaction between positive emotion and inhibition helps to provide a mechanism underlying previous results on the negative consequences of deliberation for cooperation: deliberation may undermine cooperative behavior by dampening the socially beneficial effects of positive emotion.

We build on previous work related to positive emotion and prosociality [68, 69, 71, 73] by focusing on an objective and quantitative analysis of naturally occurring emotional behavior, as opposed to deriving measures of emotion from self-reported indices subject to demand characteristics and inherent biases in questionnaire measurement of emotion responding. By combining analysis of narrative text with play in economic games for the first time, we bridge between different experimental traditions, and shed light on actual (rather than hypothetical) decision-making. The positive link our work suggests between positive emotion and cooperation is consistent with evidence that inducing gratitude increases cooperation [93], and more generally suggests the importance of manipulations that can promote positive emotions with salutary social effects such as compassion [24] and elevation [94, 95].

Our use of automated text analysis allowed us to quantitatively represent the emotional content of our participants’ narratives, and avoids potential coder biases. Our results linking inhibitory language to selfishness are consistent with work analyzing the testimony of Carnegie Heroes, people who risked their lives to save strangers [63]. The same LIWC algorithm used here found much less inhibitory language in the heroes’ descriptions of their decision process compared to deliberative control statements, suggesting that altruistic action requires lack of inhibition. Our LIWC results are also consistent with another text analysis of post-game narratives which looked for words that were significantly more common among one group of participants compared to another [96], and found that participants primed to be more intuitive were more likely to use words such as “feel”, “good”, “hope”, and “give”, and that use of these words in turn predicted cooperation.

The present results should be interpreted within the confines of several limitations. First, we note that our measures of positive (and negative) emotion were obtained at a general valence level, which precluded us from providing insight into the specific positive emotions involved in cooperation. Exploring this issue is an important direction for future work, given divergent associations between self-focused emotion such as pride and other-focused emotions like compassion on social intuitions [97]. Secondly, our measures were obtained using the default LIWC dictionaries. Although these dictionaries have been previously validated [98], their definitions of positive emotion and inhibition may not match perfectly with how these constructs are used in the fields relevant for the present research. Thus future work might explore the effect of analyzing narratives using custom designed dictionaries.

Supporting Information

S1 Dataset. Raw data in csv format for Study 1.



S2 Dataset. Raw data in csv format for Study 2.



Author Contributions

Conceived and designed the experiments: DR GKT. Performed the experiments: DR GKT. Analyzed the data: DR JG GKT. Wrote the paper: DR JG.


  1. 1. Shariff AF, Norenzayan A (2007) God Is Watching You: Priming God Concepts Increases Prosocial Behavior in an Anonymous Economic Game. Psychological Science 18: 803–809. doi: 10.1111/j.1467-9280.2007.01983.x.
  2. 2. Balliet D, Van Lange PAM (2013) Trust, Punishment, and Cooperation Across 18 Societies: A Meta-Analysis. Perspectives on Psychological Science 8: 363–379. doi: 10.1177/1745691613488533.
  3. 3. Apicella CL, Marlowe FW, Fowler JH, Christakis NA (2012) Social networks and cooperation in hunter-gatherers. Nature 481: 497–501. doi: 10.1038/nature10736. pmid:22281599
  4. 4. Rand DG, Nowak MA (2013) Human Cooperation. Trends in Cognitive Sciences 17: 413–425. doi: 10.1016/j.tics.2013.06.003. pmid:23856025
  5. 5. Herrmann B, Thoni C, Gächter S (2008) Antisocial punishment across societies. Science 319: 1362–1367. doi: 10.1126/science.1153808. pmid:18323447
  6. 6. Sigmund K (2010) The calculus of selfishness. Princeton: Princeton Univ Press.
  7. 7. Nowak MA (2006) Five rules for the evolution of cooperation. Science 314: 1560–1563. doi: 10.1126/science.1133755. pmid:17158317
  8. 8. Crockett MJ (2013) Models of morality. Trends in cognitive sciences 17: 363–366. doi: 10.1016/j.tics.2013.06.005. pmid:23845564
  9. 9. Cushman, F, Macindoe O. The coevolution of punishment and prosociality among learning agents; 2009.
  10. 10. Jordan JJ, Peysakhovich A, Rand DG ( In press) Why we cooperate. In: Decety J, Wheatley T, editors. The Moral Brain: Multidisciplinary Perspectives. Cambridge, MA: MIT Press.
  11. 11. Hauser OP, Rand DG, Peysakhovich A, Nowak MA (2014) Cooperating with the future. Nature 511: 220–223. doi: 10.1038/nature13530. pmid:25008530
  12. 12. Perc M, Gómez-Gardeñes J, Szolnoki A, Floría LM, Moreno Y (2013) Evolutionary dynamics of group interactions on structured populations: a review. Journal of The Royal Society Interface 10. doi: 10.1098/rsif.2012.0997
  13. 13. Rand DG, Arbesman S, Christakis NA (2011) Dynamic social networks promote cooperation in experiments with humans. Proceedings of the National Academy of Sciences 108: 19193–19198. doi: 10.1073/pnas.1108243108.
  14. 14. Perc M, Szolnoki A (2010) Coevolutionary games—A mini review. Biosystems 99: 109–125. doi: 10.1016/j.biosystems.2009.10.003. pmid:19837129
  15. 15. Yoeli E, Hoffman M, Rand DG, Nowak MA (2013) Powering up with indirect reciprocity in a large-scale field experiment. Proceedings of the National Academy of Sciences 110: 10424–10429. doi: 10.1073/pnas.1301210110.
  16. 16. Capraro V (2013) A Model of Human Cooperation in Social Dilemmas. PLoS ONE.
  17. 17. Rand DG, Nowak MA, Fowler JH, Christakis NA (2014) Static Network Structure Can Stabilize Human Cooperation. Proceedings of the National Academy of Sciences.
  18. 18. Crockett MJ, Kurth-Nelson Z, Siegel JZ, Dayan P, Dolan RJ (2014) Harm to others outweighs harm to self in moral decision making. Proceedings of the National Academy of Sciences.
  19. 19. Batson CD, Duncan BD, Ackerman P, Buckley T, Birch K (1981) Is empathic emotion a source of altruistic motivation? Journal of personality and Social Psychology 40: 290. doi: 10.1037/0022-3514.40.2.290.
  20. 20. Marsh AA, Kozak MN, Ambady N (2007) Accurate identification of fear facial expressions predicts prosocial behavior. Emotion 7: 239–251. doi: 10.1037/1528-3542.7.2.239. pmid:17516803
  21. 21. de Hooge IE, Zeelenberg M, Breugelmans SM (2007) Moral sentiments and cooperation: Differential influences of shame and guilt. Cognition & Emotion 21: 1025–1042. doi: 10.1080/02699930600980874.
  22. 22. DeSteno D (2009) Social Emotions and Intertemporal Choice “Hot” Mechanisms for Building Social and Economic Capital. Current Directions in Psychological Science 18: 280–284. doi: 10.1111/j.1467-8721.2009.01652.x.
  23. 23. Galinsky AD, Maddux WW, Gilin D, White JB (2008) Why It Pays to Get Inside the Head of Your Opponent: The Differential Effects of Perspective Taking and Empathy in Negotiations. Psychological Science 19: 378–384. doi: 10.1111/j.1467-9280.2008.02096.x. pmid:18399891
  24. 24. Goetz JL, Keltner D, Simon-Thomas E (2010) Compassion: An evolutionary analysis and empirical review. Psychological Bulletin 136: 351–374. doi: 10.1037/a0018807. pmid:20438142
  25. 25. van den Bos K, Van Lange PA, Lind EA, Venhoeven LA, Beudeker DA, et al. (2011) On the benign qualities of behavioral disinhibition: because of the prosocial nature of people, behavioral disinhibition can weaken pleasure with getting more than you deserve. J Pers Soc Psychol 101: 791–811. doi: 10.1037/a0023556. pmid:21574725
  26. 26. Pfeiffer T, Tran L, Krumme C, Rand DG (2012) The value of reputation. Journal of the Royal Society Interface 9: 2791–2797. doi: 10.1098/rsif.2012.0332.
  27. 27. Dreber A, Ellingsen T, Johannesson M, Rand DG (2013) Do people care about social context? Framing effects in dictator games. Experimental Economics 16: 349–371. doi: 10.1007/s10683-012-9341-9.
  28. 28. Zaki J, Mitchell JP (2013) Intuitive Prosociality. Current Directions in Psychological Science 22: 466–470. doi: 10.1177/0963721413492764.
  29. 29. Rand DG, Dreber A, Haque OS, Kane RJ, Nowak MA, et al. (2014) Religious motivations for cooperation: an experimental investigation using explicit primes. Religion, Brain & Behavior 4: 31–48. doi: 10.1080/2153599X.2013.775664.
  30. 30. Capraro V, Jordan JJ, Rand DG (2014) Heuristics guide the implementation of social preferences in one-shot Prisoner’s Dilemma experiments. Scientific Reports 4: 6790. doi: 10.1038/srep06790. pmid:25348470
  31. 31. Evans AM, Dillon KD, Rand DG (2014) Reaction Times and Reflection in Social Dilemmas: Extreme Responses are Fast, But Not Intuitive. Available at SSRN: http://ssrncom/abstract=2436750.
  32. 32. Gray K, Rand DG, Ert E, Lewis K, Hershman S, et al. (2014) The Emergence of “Us and Them” in 80 Lines of Code Modeling Group Genesis in Homogeneous Populations. Psychological science: 0956797614521816.
  33. 33. Rand DG, Yoeli E, Hoffman M (2014) Harnessing Reciprocity to Promote Cooperation and the Provisioning of Public Goods. Policy Insights from the Behavioral and Brain Sciences 1: 263–269. doi: 10.1177/2372732214548426.
  34. 34. Camerer CF (2003) Behavioral game theory: Experiments in strategic interaction Princeton, NJ: Princeton University Press.
  35. 35. Peysakhovich A, Nowak MA, Rand DG (2014) Humans Display a 'Cooperative Phenotype' that is Domain General and Temporally Stable. Nature Communications 5: 4939. doi: 10.1038/ncomms5939. pmid:25225950
  36. 36. Sloman SA (1996) The empirical case for two systems of reasoning. Psychological Bulletin 119: 3. doi: 10.1037/0033-2909.119.1.3.
  37. 37. Kahneman D (2011) Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux.
  38. 38. Kahneman D (2003) A perspective on judgment and choice: Mapping bounded rationality. American Psychologist 58: 697–720. doi: 10.1037/0003-066X.58.9.697. pmid:14584987
  39. 39. Frederick S (2005) Cognitive Reflection and Decision Making. The Journal of Economic Perspectives 19: 25–42. doi: 10.1257/089533005775196732.
  40. 40. Stanovich KE, West RF (1998) Individual Differences in Rational Thought. Journal of Experimental Psychology: General 127: 161–188. doi: 10.1037/0096-3445.127.2.161.
  41. 41. Miller EK, Cohen JD (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience 24: 167–202. doi: 10.1146/annurev.neuro.24.1.167. pmid:11283309
  42. 42. Chaiken S, Trope Y (1999) Dual-process theories in social psychology. New York: Guilford Press.
  43. 43. Rand DG, Peysakhovich A, Kraft-Todd GT, Newman GE, Wurzbacher O, et al. (2014) Social Heuristics Shape Intuitive Cooperation. Nature Communications 5: 3677. doi: 10.1038/ncomms4677. pmid:24751464
  44. 44. Rand DG, Greene JD, Nowak MA (2012) Spontaneous giving and calculated greed. Nature 489: 427–430. doi: 10.1038/nature11467. pmid:22996558
  45. 45. Rand DG, Kraft-Todd GT (2014) Reflection Does Not Undermine Self-Interested Prosociality. Frontiers in Behavioral Neuroscience 8: 300. doi: 10.3389/fnbeh.2014.00300. pmid:25232309
  46. 46. Rand DG, Newman GE, Wurzbacher O (2014) Social context and the dynamics of cooperative choice. Journal of Behavioral Decision Making. doi: 10.1002/bdm.1837.
  47. 47. Cone J, Rand DG ( In press) Time pressure increases cooperation in competitively framed social dilemmas. PLoS ONE.
  48. 48. Schulz JF, Fischbacher U, Thöni C, Utikal V (2014) Affect and fairness: Dictator games under cognitive load. Journal of Economic Psychology 41: 77–87. doi: 10.1016/j.joep.2012.08.007.
  49. 49. 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: 1080–1090. doi: 10.1177/0146167211405996. pmid:21518808
  50. 50. Roch SG, Lane JAS, Samuelson CD, Allison ST, Dent JL (2000) Cognitive Load and the Equality Heuristic: A Two-Stage Model of Resource Overconsumption in Small Groups. Organizational Behavior and Human Decision Processes 83: 185–212. doi: 10.1006/obhd.2000.2915. pmid:11056068
  51. 51. Lotz S (2014) Spontaneous Giving Under Structural Inequality: Intuition Promotes Cooperation in Asymmetric Social Dilemmas. Available: http://ssrncom/abstract=2513498.
  52. 52. Kovarik J (2009) Giving it now or later: Altruism and discounting. Economics Letters 102: 152–154. doi: 10.1016/j.econlet.2008.12.005.
  53. 53. Dreber A, Fudenberg D, Levine DK, Rand DG (2014) Altruism and Self-Control. Available at SSRN.
  54. 54. Ruff CC, Ugazio G, Fehr E (2013) Changing Social Norm Compliance with Noninvasive Brain Stimulation. Science 342: 482–484. doi: 10.1126/science.1241399. pmid:24091703
  55. 55. Tinghög G, Andersson D, Bonn C, Böttiger H, Josephson C, et al. (2013) Intuition and cooperation reconsidered. Nature 497: E1–E2. doi: 10.1038/nature12194
  56. 56. Verkoeijen PPJL, Bouwmeester S (2014) Does Intuition Cause Cooperation? PLoS ONE 9: e96654. doi: 10.1371/journal.pone.0096654. pmid:24801381
  57. 57. Hauge KE, Brekke KA, Johansson L-O, Johansson-Stenman O, Svedsäter H (2014) Keeping others in our mind or in our heart? Distribution games under cognitive load. University of Gothenburg Working Papers in Economics.
  58. 58. Kinnunen SP, Windmann S (2013) Dual-processing altruism. Frontiers in Psychology 4: 1–8. doi: 10.3389/fpsyg.2013.00193.
  59. 59. Engel C, Rand DG (2014) What does “clean” really mean? The implicit framing of decontextualized experiments. Economics Letters 122: 386–389. doi: 10.1016/j.econlet.2013.12.020.
  60. 60. Peysakhovich A, Rand DG (2014) Habits of Virtue: Creating Norms of Cooperation and Defection in the Laboratory. Available: http://ssrncom/abstract=2294242.
  61. 61. Small DA, Loewenstein G, Slovic P (2007) Sympathy and callousness: The impact of deliberative thought on donations to identifiable and statistical victims. Organizational Behavior and Human Decision Processes 102: 143–153. doi: 10.1016/j.obhdp.2006.01.005.
  62. 62. Righetti F, Finkenauer C, Finkel EJ (2013) Low Self-Control Promotes the Willingness to Sacrifice in Close Relationships. Psychological Science.
  63. 63. Rand DG, Epstein ZG (2014) Risking Your Life Without a Second Thought: Intuitive Decision-Making and Extreme Altruism. PLoS ONE 9: e109687. doi: 10.1371/journal.pone.0109687. pmid:25333876
  64. 64. Bowles S, Gintis H (2002) Prosocial emotions. In: Durlauf LBaSN, editor. The Economy as a Evolving Complex System 3. pp. 339–364. doi: 10.1093/acprof:oso/9780195162592.003.0014
  65. 65. Bowles S, Gintis H (2003) Origins of human cooperation. Genetic and cultural evolution of cooperation: 429–443.
  66. 66. Chudek M, Henrich J (2011) Culture gene coevolution, norm-psychology and the emergence of human prosociality. Trends in cognitive sciences 15: 218–226. doi: 10.1016/j.tics.2011.03.003. pmid:21482176
  67. 67. Van Lange PA, De Bruin E, Otten W, Joireman JA (1997) Development of prosocial, individualistic, and competitive orientations: theory and preliminary evidence. Journal of personality and social psychology 73: 733. doi: 10.1037/0022-3514.73.4.733. pmid:9325591
  68. 68. Gruber J, Mauss IB, Tamir M (2011) A dark side of happiness? How, when, and why happiness is not always good. Perspectives on Psychological Science 6: 222–233. doi: 10.1177/1745691611406927.
  69. 69. Lyubomirsky S, King L, Diener E (2005) The benefits of frequent positive affect: does happiness lead to success? Psychological bulletin 131: 803. doi: 10.1037/0033-2909.131.6.803. pmid:16351326
  70. 70. Seligman ME, Csikszentmihalyi M (2000) Positive psychology: An introduction: American Psychological Association.
  71. 71. Fredrickson BL (1998) What good are positive emotions? Review of general psychology 2: 300. doi: 10.1037/1089-2680.2.3.300. pmid:21850154
  72. 72. Algoe SB, Gable SL, Maisel NC (2010) It’s the little things: Everyday gratitude as a booster shot for romantic relationships. Personal relationships 17: 217–233. doi: 10.1111/j.1475-6811.2010.01273.x.
  73. 73. Shiota MN, Campos B, Keltner D, Hertenstein MJ (2004) Positive emotion and the regulation of interpersonal relationships. The regulation of emotion: 127–155.
  74. 74. Berry DS, Hansen JS (1996) Positive affect, negative affect, and social interaction. Journal of Personality and Social Psychology 71: 796. doi: 10.1037/0022-3514.71.4.796.
  75. 75. Gonzaga GC, Keltner D, Londahl EA, Smith MD (2001) Love and the commitment problem in romantic relations and friendship. Journal of personality and social psychology 81: 247. doi: 10.1037/0022-3514.81.2.247. pmid:11519930
  76. 76. Isen AM, Clark M, Schwartz MF (1976) Duration of the effect of good mood on helping: “Footprints on the sands of time.”. Journal of Personality and Social Psychology 34: 385. doi: 10.1037/0022-3514.34.3.385.
  77. 77. Strayer J (1980) A naturalistic study of empathic behaviors and their relation to affective states and perspective-taking skills in preschool children. Child Development.
  78. 78. Eisenberg N, Miller PA (1987) The relation of empathy to prosocial and related behaviors. Psychological bulletin 101: 91. doi: 10.1037/0033-2909.101.1.91. pmid:3562705
  79. 79. Zaki J, Bolger N, Ochsner K (2008) It Takes Two The Interpersonal Nature of Empathic Accuracy. Psychological Science 19: 399–404. doi: 10.1111/j.1467-9280.2008.02099.x. pmid:18399894
  80. 80. DeSteno D, Bartlett MY, Baumann J, Williams LA, Dickens L (2010) Gratitude as moral sentiment: Emotion-guided cooperation in economic exchange. Emotion 10: 289. doi: 10.1037/a0017883. pmid:20364907
  81. 81. Pennebaker JW, Francis ME, Booth RJ (2001) Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates: 71.
  82. 82. Amir O, Rand DG, Gal YK (2012) Economic Games on the Internet: The Effect of $1 Stakes. PLoS ONE 7: e31461. doi: 10.1371/journal.pone.0031461. pmid:22363651
  83. 83. Horton JJ, Rand DG, Zeckhauser RJ (2011) The Online Laboratory: Conducting Experiments in a Real Labor Market. Experimental Economics 14: 399–425. doi: 10.1007/s10683-011-9273-9.
  84. 84. Paolacci G, Chandler J, Ipeirotis PG (2010) Running Experiments on Amazon Mechanical Turk. Judgment and Decision Making 5: 411–419.
  85. 85. Buhrmester MD, Kwang T, Gosling SD (2011) Amazon′s Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data? Perspectives on Psychological Science 6: 3–5. doi: 10.1177/1745691610393980.
  86. 86. Rand DG (2012) The promise of Mechanical Turk: How online labor markets can help theorists run behavioral experiments. Journal of theoretical biology 299: 172–179. doi: 10.1016/j.jtbi.2011.03.004. pmid:21402081
  87. 87. Mehl MR, Pennebaker JW (2003) The sounds of social life: a psychometric analysis of students' daily social environments and natural conversations. Journal of personality and social psychology 84: 857. doi: 10.1037/0022-3514.84.4.857. pmid:12703653
  88. 88. Pennebaker JW (1993) Putting stress into words: Health, linguistic, and therapeutic implications. Behaviour research and therapy 31: 539–548. doi: 10.1016/0005-7967(93)90105-4. pmid:8347112
  89. 89. Pennebaker JW (1997) Writing about emotional experiences as a therapeutic process. Psychological science 8: 162–166. doi: 10.1111/j.1467-9280.1997.tb00403.x.
  90. 90. Gruber J, Keltner D (2007) Emotional Behavior and Psychopathology: A Survey of Methods and Concepts. In: Rottenberg J, Johnson SL, editors. Emotion and Psychopathology: Bridging Affective and Clinical Science. Washington, D.C: American Psychological Association (APA) Books.
  91. 91. Pennebaker JW, Kiecolt-Glaser JK, Glaser R (1988) Disclosure of traumas and immune function: health implications for psychotherapy. Journal of consulting and clinical psychology 56: 239. doi: 10.1037/0022-006X.56.2.239. pmid:3372832
  92. 92. Pennebaker JW, Mehl MR, Niederhoffer KG (2003) Psychological aspects of natural language use: Our words, our selves. Annual review of psychology 54: 547–577. doi: 10.1146/annurev.psych.54.101601.145041. pmid:12185209
  93. 93. Bartlett MY, DeSteno D (2006) Gratitude and Prosocial Behavior: Helping When It Costs You. Psychological Science 17: 319–325. doi: 10.1111/j.1467-9280.2006.01705.x. pmid:16623689
  94. 94. Haidt J (2000) The Positive emotion of elevation.
  95. 95. Schnall S, Roper J, Fessler DM (2010) Elevation leads to altruistic behavior. Psychological Science.
  96. 96. Roberts ME, Stewart BM, Tingley D, Lucas C, Leder-Luis J, et al. ( In press) Topic models for open ended survey responses with applications to experiments. American Journal of Political Science.
  97. 97. Oveis C, Horberg E, Keltner D (2010) Compassion, pride, and social intuitions of self-other similarity. Journal of personality and social psychology 98: 618. doi: 10.1037/a0017628. pmid:20307133
  98. 98. Tausczik YR, Pennebaker JW (2010) The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology 29: 24–54. doi: 10.1177/0261927X09351676.