Cortical thickness of the dorsolateral prefrontal cortex predicts ... - PNAS

Cortical thickness of the dorsolateral prefrontal cortex predicts strategic choices in economic games Toshio Yamagishia,b,1, Haruto Takagishib, Alan de Souza Rodrigues Ferminb, Ryota Kanaic, Yang Lib, and Yoshie Matsumotob a Graduate School of International Corporate Strategy, Hitotsubashi University, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8439, Japan; bBrain Science Institute, Tamagawa University, 6-1-1 Tamagawagakuen, Machida, Tokyo 194-8610, Japan; and cDepartment of Neuroinformatics, Araya Brain Imaging, 3-16-16 Daizawa, Setagayaku, Tokyo 155-0032, Japan

Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved March 30, 2016 (received for review December 5, 2015)

Human prosociality has been traditionally explained in the social sciences in terms of internalized social norms. Recent neuroscientific studies extended this traditional view of human prosociality by providing evidence that prosocial choices in economic games require cognitive control of the impulsive pursuit of self-interest. However, this view is challenged by an intuitive prosociality view emphasizing the spontaneous and heuristic basis of prosocial choices in economic games. We assessed the brain structure of 411 players of an ultimatum game (UG) and a dictator game (DG) and measured the strategic reasoning ability of 386. According to the reflective norm-enforcement view of prosociality, only those capable of strategically controlling their selfish impulses give a fair share in the UG, but cognitive control capability should not affect behavior in the DG. Conversely, we support the intuitive prosociality view by showing for the first time, to our knowledge, that strategic reasoning and cortical thickness of the dorsolateral prefrontal cortex were not related to giving in the UG but were negatively related to giving in the DG. This implies that the uncontrolled choice in the DG is prosocial rather than selfish, and those who have a thicker dorsolateral prefrontal cortex and are capable of strategic reasoning (goal-directed use of the theory of mind) control this intuitive drive for prosociality as a means to maximize reward when there are no future implications of choices.

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ultimatum game dictator game prosocial behavior

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umans are a cooperative species, and the question of why humans are so cooperative has been a subject of considerable interest in social and biological sciences (1–4). The traditional answer in the social sciences highlights critical roles of social norms and cultural values internalized as personal values and social preferences (5, 6). Recent neuroscientific studies of brain structure and activity extended this traditional view of human prosociality by showing that players of economic games act prosocially when they cognitively control selfish impulses (7–13). Experimental evidence shows that prosocial choices in economic games positively relate to local gray matter volume and thickness and the activation of brain areas that control selfish impulsive drives, such as the dorsolateral prefrontal cortex (DLPFC) and temporoparietal junction (TPJ) (7–9). Furthermore, impairment of cognitive control by disruption of DLPFC function prevents rejection of unfair offers in the ultimatum game (UG), which some authors considered prosocial and fairness-seeking behavior (10– 13). Recently, this reflective view of human prosociality has been challenged by an alternative view emphasizing the intuitive nature of prosocial behavior, subsumed under intuitive prosociality (14) or heuristic cooperation (15–17). Support for the intuitive and automatic nature of prosocial behavior is provided by findings that prosocial choices are promoted under time pressure (15, 16, 18), under cognitive load (19–21), or after priming by successful experiences of intuitive decision making (15, 22). Also, participants who expressed more positive emotional words and less inhibitory words during and after an economic game 5582–5587 | PNAS | May 17, 2016 | vol. 113 | no. 20

cooperated more (23). Additionally, increased activity in the lateral prefrontal cortex was negatively related to fairness-seeking behavior in an economic game (24). According to the heuristic prosociality model (14–17), humans are predisposed to cooperate in social exchange situations. People fail to behave in a prosocial manner in social exchanges when this predisposition is overridden by strategic reasoning to secure their self-interest. By comparing participants’ behaviors in two economic games with brain structural differences and strategic reasoning abilities, we provide evidence that strategic reasoning controls, and thus reduces rather than promotes, game players’ prosocial behavior. The contrast between two simple, two-person economic games— namely, the dictator game (DG) and the UG—is often used to support the reflective prosocial model by demonstrating how strategic reasoning affects game players’ decisions. In both games, one player freely decides how much of a fixed reward to take and how much to leave for the other player. The difference between the two games is that the other player in the UG (termed “responder”) has the option to reject the decision made by the first player (termed “proposer”), causing both to earn nothing. This option is not provided to the second player in the DG, who plays the role of a “recipient”. The recipient simply receives whatever the first player (“dictator”) gives. The level of giving by the proposer in the UG is usually higher than that by the dictator in the DG (25). This is attributed to the proposer’s strategic reasoning, which requires inference of the recipient’s internal state and prediction of the resulting response (e.g., anger on the basis of unfair giving and subsequent rejection) (8, 9, 13). Given that neuroimaging and neuroendocrinological studies showed that negative emotions are associated with rejection of unfair offers (24, 26, 27), UG proposers may anticipate negative responses to unfair offers. UG proposers anticipate norm-enforcing responses (rejection of the offer) to norm-violating behavior (taking most of the Significance Is human prosociality a consequence of cognitive control of selfish impulses? Alternatively, is it a default option that most people use unless they are cognitively persuaded that a given situation does not require them to behave prosocially? Our results support the latter argument. Participants with weaker cognitive control fairly shared a reward with another participant even when there was no chance of punishing unfair behavior, whereas those more capable of cognitive control behaved selfishly in the same situation. These findings demonstrate that participants’ intuitive choices in economic games are prosocial. Author contributions: T.Y., Y.L., and Y.M. designed research; H.T., Y.L., and Y.M. performed research; T.Y., H.T., A.d.S.R.F., and R.K. analyzed data; and T.Y., Y.L., and Y.M. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. 1

To whom correspondence should be addressed. Email: [email protected].

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reward) and strategically adjust giving behavior to secure acceptance by the responder. Thus, those capable of using strategic reasoning are expected to make fair offers in the UG compared with those who struggle to control their selfish drive for immediate reward. In contrast, in the DG, which requires no strategic reasoning to earn as much as possible, strategic control over selfish impulses is expected not to influence the player’s choices. Spitzer et al. (9) confirmed this by showing a positive correlation between the difference in giving in the UG and the DG (i.e., a measure of strategic reasoning) and activity of the right DLPFC and the lateral orbitofrontal cortex. Given earlier findings implicating the DLPFC in cognitive control of impulsive behavior (28–33), this is taken to support the reflective model of prosociality, in which prosocial behavior requires cognitive control of the impulsive drive toward selfish behavior. Steinbeis et al. (8) provided further support via a comparison of young children’s choices in the two economic games. Children took a large share in the DG while providing fairer amounts to responders in the UG. The children’s more generous giving in the UG may be based upon strategic reasoning regarding the possible consequences of not giving enough in the UG—that is, receiving no reward due to rejection by the other child—which plays no role in the DG. Thus, the difference in giving between the UG and the DG is considered to reflect the use of strategic reasoning in the UG. The strategic choices of more giving in the UG than in the DG is related to children’s age, cortical thickness, and activity of their left DLPFC. As children age and their DLPFC develops further, they become able to control their selfish drive and adjust their behavior to the anticipated negative consequences. This interpretation of UG–DG difference in prosocial giving as a reflection of strategic reasoning (8, 9) assumes that the default choice in the DG is impulsive and selfish. Younger children and those with a thinner DLPFC are presumably less capable of strategically adjusting their decisions to deal with anticipated responses and would impulsively pursue their own benefits in both the UG and the DG. In contrast, older children and those with a thicker DLPFC are more likely to have enhanced cognitive control, which can be used to strategically adjust their choices, especially in the UG but not in the DG. Therefore, a UG–DG positive reward transfer difference is produced by strategists’ control over the selfish impulses in the UG, whereas those who fail to control such impulses in the UG claim a considerable share in both games (Fig. 1A). In contrast, the alternative, intuitive prosociality model assumes that the uncontrolled choice is prosocial in both the UG and the DG, rather than selfish. Strategists control this impulse toward prosociality in the DG where immediate pursuit of self-interest causes no strategic problem (Fig. 1B). Nonstrategists do not control this impulse and provide a fair share in both games. Thus, a difference due to strategic reasoning is predicted to exist in the DG but not in the UG. The reflective and intuitive prosociality models Yamagishi et al.

Results Cortical Thickness and Game Behavior. The participants gave a

mean proportion of the endowment of 0.410 (SE = 0.007) in the UG, which was significantly higher than the mean proportion in the DG (M = 0.324, SE = 0.010), t(410) = 8.97, P < 0.0001. These results agreed with previous findings (8, 9). We first sought to replicate the earlier finding of the positive correlation between cortical thickness of the DLPFC and strategic behavior, shown as difference in money provided to the partner in the UG and DG. Cortical thickness was estimated using the FreeSurfer package (see Methods), and the cerebral cortex parcellated according to the Destrieux Anatomical Atlas (36). Given that neuroimaging studies with humans associate the DLFPC broadly with the middle and superior frontal gyri (37), we liberally matched the DLPFC to regions 15 (middle frontal gyrus), 16 (superior frontal gyrus), 52 (inferior frontal sulcus), 53 (middle frontal sulcus), and 54 (superior frontal sulcus) of the Destrieux Atlas (see Fig. 2A), with the aim of identifying component regions of the DLPFC related to strategic behavior. More specifically, we focused on the middle frontal gyrus as the DLPFC because it contains cytoarchitectonic regions (Brodmann area 46 and 9) classically identified as the DLPFC in macaques and humans (38). Significant correlations were found between strategic behavior and cortical thickness of the right (r = 0.117, P = 0.018) and left (r = 0.121, P = 0.015) DLPFCs, after adjusting for age, sex, and intracranial volume (ICV) (Fig. 2B), replicating earlier findings (8). A similar relationship was also found for the superior frontal gyrus (Fig. S1). Relationships between giving in the two games and cortical thickness were also found with regard to the left and right superior frontal sulcus, although they were less clear for the latter (Fig. S2). Neither cortical thickness of the middle frontal sulcus (Fig. S3) nor the inferior frontal sulcus (Fig. S4) were correlated with strategic behavior or the level of giving in the two games. We then tested the relationships between DLPFC thickness and proportions of giving in the UG and DG separately. Neither the cortical thickness of the right nor the left DLPFC significantly correlated with the level of giving in the UG (Fig. 2 C and D). In contrast, cortical thickness significantly negatively correlated with the level of giving in the DG (Fig. 2 C and D). In a repeated-measures analysis of variance including interaction between the two games, controlling for age, sex, and ICV, the interaction effect was significant—rDLPFC, F(1, 406) = 5.68, P = 0.018, PNAS | May 17, 2016 | vol. 113 | no. 20 | 5583

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Fig. 1. Schematic representations of how strategic considerations generate the difference between strategists (ST) and nonstrategists (Non-ST) in the UG and DG. A shows the prediction that strategic considerations should improve fair behavior in the UG. B shows the prediction that strategic considerations should depress fair behavior in the DG.

thus make distinct predictions regarding the relationship between DLPFC thickness and behavior in the UG and DG. The reflective model predicts a positive relationship between DLPFC thickness and giving in the UG, whereas the intuitive model predicts a negative relationship between DLPFC thickness and giving in the DG. These two alternative accounts of differences in giving in the UG and DG (8, 9) provide a way to test the intuitive selfishness assumption against the intuitive prosociality assumption. We first successfully replicated earlier findings that strategic behavior is more pronounced among those who had a thicker DLPFC than those who had a thinner DLPFC (8) in a study of 411 adult, nonstudent participants who played both the UG and DG and from whom brain structural images were obtained. Then, we found for the first time, to our knowledge, that local gray matter thickness of the DLPFC negatively correlated with giving in the DG but was not correlated with giving in the UG (Fig. 2 C and D). We further measured the strategic reasoning of 386 of these participants using a newly developed test of strategic reasoning, measured 411 participants’ Machiavellianism (34, 35) score, and found that those exhibiting better strategic reasoning behaved more selfishly in the DG than those with poor strategic reasoning, but no relationship was found between task performance and fairness in the UG. These striking findings provide strong evidence supporting the intuitive prosociality prediction depicted in Fig. 1B but not the reflective prosociality prediction shown in Fig. 1A.

Fig. 2. Brain areas in the Destrieu Atlas (A), the relationship of cortical thickness of the DLPFC (middle frontal gyrus) and strategic choice (UG–DG) (B), the relationship of its right cortical thickness and giving in the UG and DG (C), and the relationship of its left cortical thickness and giving in the UG and DG (D). The horizontal axis represents the residual cortical thickness adjusted for participants’ age, sex, and ICV. The vertical axis represents the mean strategic choice of the players, giving in the UG or the DG within 0.1-mm intervals of residual cortical thickness. Each interval spans 0.1 mm on the horizontal axis segment. The size of each circle shows the number of players who fell within the interval. Error bars are SEs. n = 411. Correlations are after adjusting for age, sex, and ICV.

and lDLPFC, F(1, 406) = 5.99, P = 0.015. These findings are consistent with the prediction based on the intuitive prosociality model illustrated in Fig. 1B and inconsistent with the reflexive prosociality model prediction (Fig. 1A). Strategic Reasoning and Game Behavior. We further tested the predictions, illustrated in Fig. 1 A and B, specifying how behaviors in the UG and the DG are related to strategic decision making. Despite suggestions that differences in choice behavior in the UG may reflect the use of theory of mind to anticipate others’ reactions, no direct evidence supported this. We first tested participants’ strategic reasoning using the Machiavelli (Mac) game, which requires forward planning to select the choice that maximizes own reward based on estimation of others’ mental states and likely choices. Participants played the Mac game eight times with two other players, each time with a different set of parameters, and without feedback after each trial. One strategic response in each trial would give the participant the highest monetary reward if he or she could successfully infer other two players’ choices and influence their decisions using monetary incentives (see Methods for further details). According to a binomial distribution of random choices with a probability of 0.5 for each choice, the probability of making seven (P = 0.046) or eight (P = 0.004) strategic responses in the eight trials was less than 0.05. We thus classified those whose number of optimal strategic responses were seven or eight as strategists and those whose number of optimal strategic responses was six or less as nonstrategists. The proportion of strategists was 0.352 (see Fig. S5 for the distribution of the number of strategic responses). Strategists had a higher level of strategic behavior (i.e., UG–DG giving) compared with nonstrategists, t(239.7) = 3.36, P < 0.001 (see Fig. 3). Consistent with heuristic prosociality model predictions (Fig. 1B), there was no significant difference in the level of giving between strategists and nonstrategists in the UG, t(384) = 0.05, P = 0.959 (Fig. 3). Conversely, in the DG, strategists’ level of giving was significantly lower than nonstrategists, t(384) = 3.21, P = 0.001 (Fig. 3). The interaction effect of the UG versus DG and the dichotomized Mac game score was highly significant, F(1, 384) = 12.52, P < 0.001. The same conclusions were drawn when the original Mac game scores instead of the dichotomized scores were used (UG: r = –0.009, P = 0.854; DG: r = –0.163, P = 0.001; interaction: F(1, 384) = 11.43, P < 0.001). Although strategists’ mean IQ was higher than nonstrategists’ (105.70 vs. 95.86), t(384) = 7.66, 5584 | www.pnas.org/cgi/doi/10.1073/pnas.1523940113

P < 0.0001, IQ was not significantly correlated with strategic behavior (UG–DG; r = 0.080, P = 0.105), UG proposal (r = –0.031, P = 0.529), or DG giving (r = –0.096, P = 0.053). Regression analyses of strategic behavior indicated that the effect of strategists versus nonstrategists remained highly significant after controlling for IQ (β = 0.069), t(383) = 3.17, P = 0.002. Machiavellian Personality and Game Behavior. We successfully replicated the earlier finding (9) of a positive correlation between Machiavellian personality score and strategic behavior (r = 0.185, P < 0.001). Although Machiavellian personality score was negatively correlated with giving in both the DG (r = –0.255, P < 0.0001) and UG (r = –0.121, P = 0.014), the interaction effect was significant, F(1, 409) = 14.55, P < 0.001. Given that the Machiavellian personality scale is a mixture of strategic pursuit of self-interest and general distrust of others (39) and that the latter component was irrelevant in our analysis of strategic reasoning, we used Yamagishi’s general trust score (40) as a control variable. When general trust was controlled, Machiavellian personality score correlated with DG giving (r = –0.171, P < 0.001) but not UG proposal (r = –0.062, P = 0.214). When general trust was added as an independent variable, the interaction effect remained significant, F(1, 408) = 7.65, P = 0.006.

Discussion The reflective model of prosociality assumes that prosocial behavior requires control of selfish impulses to meet demands of

Fig. 3. Means of strategic choice (difference in giving in the UG and DG), proportion of giving in the UG, and proportion of giving in the DG. Error bars show 95% confidence interval. n = 386.

Yamagishi et al.

Yamagishi et al.

clarify the evolutionary and neuropsychological foundations of rejection of unfair UG offers, particularly in relation to internalized social norms of nonaggression versus norms of punishing normviolators. Another threat to the validity of this interpretation concerns the possible confound of general intelligence in performance in the Mac game, as implied by the correlation between IQ scores and Mac game scores (r = 0.355, P < 0.0001). However, correlations between Mac game scores and strategic behavior or DG giving remain highly significant even after adjusting for IQ, indicating that the Mac game’s assessment regarding strategic choices in the DG is independent of general intelligence. Finally, earlier studies found a positive relationship between young children’s performance on a cognitive, but not emotional, theory of mind test and the levels of proposed UG giving (50, 51). Although these findings are inconsistent with the current lack of effect of strategic reasoning in the UG, the inconsistency may be due to age differences in the earlier study population (8, 50, 51) versus the current study. Cognitive control of normative demands in the DG may require the strategic use or goal-directed mobilization of theory of mind (52). Very young children, before acquiring cognitive theory of mind, do not make fair offers in either the UG or the DG (50, 51). As they grow and acquire cognitive theory of mind, they start adjusting their behavior to anticipated responses, resulting in fair and acceptable offers in the UG. Up to this stage of cognitive development, acquisition of cognitive theory of mind promotes fair offers in the UG and engenders the pattern depicted in Fig. 1A. By the age of 5, children start internalizing others’ responses and give more in the DG when they are monitored by the experimenter than when they are not (53). The internalization of social norms and anticipated responses by others further develops throughout elementary school, as shown by the finding that DG giving increases first if the player’s choice is observed by other players, followed by a gradual increase in the situations where the player’s choice is anonymous (54). Generally, children’s giving in the DG increases as they age (54–56). Consequently, default giving in the DG increases and the pattern shifts from that in Fig. 1A to that in Fig. 1B. At the stage of full internalization of social norms, taking a large share in the DG requires goal-directed mobilization of theory of mind. Only those who understand the irrelevance of norm-abiding behavior in a one-shot, anonymous DG control the internalized demand for norm-abiding behavior and freely take a majority share. Recent studies of theory of mind reveal that many adults who are capable of theory of mind do not use it routinely (57, 58). Given DLPFC involvement in DG decision making, involvement of executive control in adult use of theory of mind (58), and the established relationship between the DLPFC and executive control (59), future studies of human prosociality should address the role of strategic (or goal-oriented) use of theory of mind in controlling adherence to default prosocial choices. Deliberate pursuit of self-interest without respect to social norms yields better outcomes for the self than automatically observing social norms, insofar as the player can accurately assess the anonymity and one-shot nature of the experiment. Assessment of the nature of real-world social interactions is more difficult than in experiments, and making errors in assessment is always possible. Erroneously assessing that selfish behavior will not be noticed in some situations, but when such behavior is actually detected, it can lead to devastating consequences—punishment or ostracism from the community. Individuals who are poor at assessing the nature of social situations may give up chances to selfishly maximize profit to reduce the probability of committing serious social errors (16, 17). From this logic of error management (17), suppressing internalized demands of social norms to pursue immediate self-interest in appropriate situations, such as the DG, can be adaptive for capable strategists who can discern salient situational aspects. However, this strategy can be maladaptive for those more prone to make mistakes in assessing such situations. Those individuals avoid making errors with potentially serious consequences by always adhering to internalized PNAS | May 17, 2016 | vol. 113 | no. 20 | 5585

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social norms. Contrary to this, our findings support an alternative scenario such that cognitive control operates in the opposite direction—that is, to control the automatic demands for prosocial behavior. The UG is often considered a game in which proposers strategically adjust their choices to accommodate anticipated responses (8, 9). The player’s choices in the DG are considered a straightforward reflection of social preferences, uncontaminated by strategic considerations (8, 9, 41, 42). Our findings challenge this widely accepted understanding. Contrary to the previous understanding, the proposer’s choices in the UG are not related to the cortical thickness of the DLPFC or strategic reasoning ability. Furthermore, both the cortical thickness of the DLPFC and strategic reasoning ability were negatively associated with giving in the DG, where strategic reasoning has been understood to play no role (8, 9, 41, 42). Although these findings are counterintuitive because they oppose predictions based on the reflective model of prosociality as depicted in Fig. 1A, they are consistent with the intuitive prosociality predictions shown in Fig. 1B. Furthermore, these findings are consistent with the findings of a transcranial direct current stimulation (tDCS) study by Ruff et al. (13), who reported that anodal tDCS of the right LPFC increased strategic behavior (UG–DG, which the authors called “sanction-induced transfer”) and decreased giving in the DG while not affecting giving in the UG. Cathodal tDCS produced opposite effects, reducing strategic behavior and improving giving in the DG, again while not affecting giving in the UG. Theoretical and methodological issues and limitations of the current study need to be discussed here. The first concerns the relationship between cognitive control and cognitive assessment of the situation. We argued that participants with a thick DLPFC and an ability for strategic reasoning are better at discerning irrelevance of social norms on their own future welfare when facing an experiment where anonymity with no future interactions was ensured. Those who do not see the irrelevance of social norms for their future welfare in one-shot laboratory experiments will automatically follow the demands of social norms. We used the term “cognitive control” to refer to the individual’s inclination toward assessment of the consequences of their own action for immediate and long-term future welfare. One model of the evolution of dual-process (intuitive versus deliberative) decision makers (43) indicates that the level of this inclination depends on the nature of the social environment surrounding individuals, such as the relative frequency of repeated versus one-shot interactions. It is also unclear whether cortical thickness of the DLPFC translates into cortical function and plays a role in decision making in the UG and DG. A previous study (8) found a positive relationship between strategic behavior and cortical thickness and functional activity of the DLPFC. It is thus plausible to suggest, based on current findings, that strategists not only have thicker DLPFCs but also recruit their deliberative functions more heavily than nonstrategists. The tDCS study by Ruff et al. (13) is of particular importance here because it shows that an induced increase in LPFC excitability is causally related to reduced DG but not UG giving, suggesting that DLPFC activity is causally related to norm-violating behavior in the DG. A finding that disrupting the DLPFC made UG responders more accepting of unfair offers (10) seems to contradict the current findings if rejection of unfair UG offers is considered a norm-enforcing behavior. However, the latter assumption has been increasingly criticized (40, 44–49), and the critics provide evidence that rejection of unfair offers as emotion-based aggression is against the dominant social norm, at least in highly industrialized societies (40, 44, 45, 48, 49), and is instead a long-term strategy to protect oneself from possible future exploitation by others (47, 48). From this alternative view, not rejecting and accepting the disadvantageous offer is an internalized and intuitive norm-abiding behavior that must be controlled to execute strategies to protect the player’s reputation and standing (47–49). More studies are needed to

demands of social norms. This can be a more adaptive strategy. The adaptive advantages of intuitive prosociality and deliberative decision making discussed above also flow from a recently proposed formal model of the evolution of dual-process decision makers who cooperate by default but spend cognitive cost to assess the benefit of noncooperation (43). This model asserts that everyone spends a cognitive cost to assess the benefit of one type of decision against the other when the strategic nature of the situation is obvious, as in the UG. In other situations, as in the DG, whether or not to spend cognitive cost to discern the true one-shot nature of the game depends on the individual’s inclination toward intuitive prosociality, which reflects the nature of the social environment surrounding the player in real life. We would also like to emphasize that our finding that the cortical thickness of the DLPFC and strategic reasoning ability reduces giving in the DG but does not affect giving in the UG makes us reconsider the way we interpret the findings of the studies using these games and the way we address the evolutionary puzzle of human prosociality. Methods Sample. Five hundred and sixty-four nonstudent residents (ages 19–59 as of January 2012) living in Machida, a suburb of Tokyo, and its surroundings participated in the initial wave of a longitudinal study consisting of eight waves to date and continuing about 3.5 y from its inauguration in 2013. Of these 564, 411 (197 female) participated in both a DG experiment and a UG experiment and submitted to brain structural scans. The Mac game was conducted with 386 participants (185 female), and the Machiavellianism Scale was administered to all 411 participants who participated in the two games and received brain scans. All experimental protocols were approved by the ethics committee at the Brain Science Institute, Tamagawa University, where the study was conducted, according to the requirements of the Declaration of Helsinki; the methods were carried out in accordance with approved guidelines. One informed consent form was signed by each participant in the first wave of the study to confirm their overall agreement to participate, and another was signed separately to provide permission for the brain scan. Data from this project have been used elsewhere, but the comparison of proposals in the UG and giving in the DG has not been reported in earlier studies. Games and Measures. DG. All participants first played a one-shot DG (DG1) as dictators. Each participant was given an endowment of JPY 1,000 and decided how much of the endowment to give to their partner (the recipient). Participants actually received the money they allocated to themselves as well as the money allocated by a randomly matched dictator. Following DG1, participants played similar games six times as a dictator, with a different recipient each time. The size of the endowment varied each time, ranging from JPY 300 to JPY 1,300 (300, 400, 600, 700, 1,200, and 1,300). Participants were told that they would receive payment once as a dictator and once as a recipient. We used the mean proportion of endowment given to the recipient in the seven games as participant’s giving in the DG. UG. Each participant played the game once as a proposer and once as a responder and was actually paid the sum of the money received in two of the games. All participants played first as proposers, without knowing that they would later play the game in the other role. As proposers, participants decided how much of an endowment of JPY 1,500 to provide to a randomly matched responder, in increments of JPY 100. After all participants made their decisions as proposers, they were told that they would play the game again in the other role and decide whether to accept or reject each of 16 possible proposals (JPY 0 to JPY 1500) made by a newly matched proposer. When all participants had played the game in both roles, random pairs were formed twice, once where the participant was the proposer and once as the responder. Each participant was paid according to the choice made in each pairing. We used the proportion of JPY 1,500 the participant gave the responder as proposed giving in the UG. Mac game. A strategic choice in an interdependent situation is goal-oriented and is realized by reading the intentions of other individuals. Niccolo Machiavelli is the most well-known advocate of strategic choices, or choices that maximize one’s own benefits (i.e., the goal) by correctly determining

1. Bowles S, Gintis H (2011) A Cooperative Species (Princeton Univ Press, Princeton). 2. Boyd R, Richerson PJ (2009) Culture and the evolution of human cooperation. Philos Trans R Soc Lond B Biol Sci 364(1533):3281–3288. 3. Nowak M (2011) SuperCooperators: Altruism, Evolution, and Why We Need Each Other to Succeed (Free Press, New York).

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the responses of all relevant players. We developed a task called the Mac game to measure the player’s use of strategic choices in an interdependent situation. In this task, we defined a strategic choice as one that maximizes one’s own reward based on the expected choices of two other players, based on the assumption that other people prefer more money than less. To successfully make the strategic choice requires an understanding of the nature of interdependence between players—that is, that what a player gets depends on other players’ choices. Strategic choices in this sense require the unsolicited use of theory of mind. Studies of theory of mind with adult participants (57, 58) indicate that some adults do not in an unsolicited manner use a theory of mind that most children over the age of 6 can use, even when it must be used to successfully perform a task. Among adults, being able to understand the actions of others in terms of their mental states does not necessarily mean that this ability is used voluntarily in everyday decisionmaking. In the Mac game, correct prediction of the other players’ choices is required to earn the most money. Another feature of the Mac game is that the players must understand that they can influence other players’ choices. For example, player A has a choice between a1 and a2. If A chooses a1, B earns $10 (not A’s money). If A chooses a2, B earns $1. It is thus in B’s best interest to induce A to choose a1 instead of a2. B has the opportunity to indicate to A that he or she will pay A $1 if A chooses a1 and nothing if A chooses a2. Paying A $1 for choosing a1 will induce A to choose that option, and consequently, B will earn $10. In this simplified example, paying $1 to A so that he or she chooses a1 is a strategic choice, which requires that B understands he or she can influence A’s choice such that A will choose a1. The Mac game involves two steps of this type of reasoning and is thus more complex than this simple example. It is explained fully in SI Methods: The Mac Game and Dataset S1 for the actual instructions used in the study. IQ. We administered an IQ test (Kyoto University NX15 (60) in wave 1, which took about 1.5 h, including instructions. General trust. Yamagishi’s general trust scale (40) was administered three times in waves 1, 3, and 6, and the mean of the three measures of general trust after standardizing each was used as the overall measure of general trust. MRI Data Acquisition. MRI images were recorded on a 3 Tesla Siemens Trio A Tim MRI scanner. High-resolution anatomical images were acquired using a T1-weighted 3D magnetization prepared rapid acquisition gradient echo sequence (repetition time, 2,000 ms; echo time, 1.98 ms; field of view, 256 × 256 mm; number of slices, 192; voxel size, 1 × 1 × 1 mm; average, 3 times). MRI Data Analysis. Gray matter thickness of the regions labeled as the middle frontal gyrus, middle frontal sulcus, superior frontal sulcus, inferior frontal sulcus, and superior frontal gyrus was extracted as the volume of the DLPFC. Gray matter thickness values were estimated for the five regions on both hemispheres using the FreeSurfer package (version 5.1.0 for Linux CentOS 4; surfer.nmr.mgh. harvard.edu). Three T1-weighted MRI images were registered and averaged for each participant. The mean images were submitted to a fully automated procedure that reconstructed 3D models of the pial surface and the boundary between the gray and white matter. The initial part of the reconstruction procedure included registration to a common stereotactic space, image intensity correction for magnetic field inhomogeneity, and skull stripping. The boundary between the gray and white matter for each hemisphere was segmented, tessellated, and corrected for topological errors. The resulting surface models of the boundary were aligned to a surface template by matching the gyral and sulcal patterns to the template. We computed the cortical thickness of the regions of interest using cortical parcellation based on the Destrieux Atlas (36), which divides each cortical hemisphere into 74 regions. Gray matter thickness was calculated as the closest distance between the gray/white matter boundary and the pial surface. We used the Destrieux Atlas to match the DLPFC with the regions specified in the atlas. Specifically, the DLPFC was matched with regions 15 (middle frontal gyrus), 16 (superior frontal gyrus), 52 (inferior frontal sulcus), 53 (middle frontal sulcus), and 54 (superior frontal sulcus) in the Destrieux Atlas (see Fig. 2A). All relevant data used for analysis are included in Dataset S2. ACKNOWLEDGMENTS. The studies reported in this paper were supported by Grants-in-aid 23223003 and 15H05730. We thank Profs. Minoru Kimura and Masamichi Sakagami (Tamagawa University Brain Science Institute) for their continuous support.

4. Tomasello M, Melis AP, Tennie C, Wyman E, Hermann E (2012) Two key steps in the evolution of human cooperation: The interdependence hypothesis. Curr Anthropol 53(6):673–692. 5. Mead G (1934) Mind, Self, and Society (University of Chicago Press, Chicago). 6. Parsons T (1937) The Structure of Social Action (McGraw Hill, New York).

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33. Miller EK, Cohen JD (2001) An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24:167–202. 34. Nakamura T, et al. (2012) Development and validation of a Japanese version of the Machiavellianism Scale. Jpn J Pers 20(3):233–235. 35. Christie R, Geis FL (1970) Studies in Machiavellianism (Academic, New York). 36. Destrieux C, Fischl B, Dale A, Halgren E (2010) Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1): 1–15. 37. Barbey AK, Colom R, Grafman J (2013) Dorsolateral prefrontal contributions to human intelligence. Neuropsychologia 51(7):1361–1369. 38. Passingham RE, Wise SP (2012) The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Thought (Oxford Univ Press, Oxford). 39. Dahling JJ, Whitaker BG, Levy PE (2009) The development and validation of a new Machiavellianism scale. J Manage 35(2):219–257. 40. Yamagishi T, et al. (2013) Is behavioral pro-sociality game-specific? Pro-social preference and expectations of pro-sociality. Organ Behav Hum Dec 120(2):260–271. 41. Andreoni J, Miller J (2002) Giving according to GARP: An experimental test of the consistency of preferences for altruism. Econometrica 70(2):737–753. 42. Fehr E, Schmidt KM (1999) A theory of fairness, competition and cooperation. Q J Econ 114(3):817–868. 43. Bear A, Rand DG (2016) Intuition, deliberation, and the evolution of cooperation. Proc Natl Acad Sci USA 113(4):936–941. 44. Eriksson K, Andersson PA, Strimling P (2015) Moderators of the disapproval of peer punishment. Group Process Intergroup Relat; epub ahead of print 10.1177/1368430215583519. 45. Espín AM, Brañas-Garza P, Herrmann B, Gamella JF (2012) Patient and impatient punishers of free-riders. Proc Biol Sci 279(1749):4923–4928. 46. Strimling P, Eriksson K (2014) Regulating the regulation: Norms about punishment. Reward and Punishment in Social Dilemmas, eds van Lange PAM, Rockenbach B, Yamagishi T (Oxford Univ Press, Oxford), pp 52–69. 47. Burnham TC (2007) High-testosterone men reject low ultimatum game offers. Proc Biol Sci 274(1623):2327–2330. 48. Yamagishi T, et al. (2009) The private rejection of unfair offers and emotional commitment. Proc Natl Acad Sci USA 106(28):11520–11523. 49. Yamagishi T, et al. (2012) Rejection of unfair offers in the ultimatum game is no evidence of strong reciprocity. Proc Natl Acad Sci USA 109(50):20364–20368. 50. Takagishi H, Kameshima S, Schug J, Koizumi M, Yamagishi T (2010) Theory of mind enhances preference for fairness. J Exp Child Psychol 105(1-2):130–137. 51. Takagishi H, et al. (2014) The role of cognitive and emotional perspective taking in economic decision making in the ultimatum game. PLoS One 9(9):e108462. 52. Sher I, Koenig M, Rustichini A (2014) Children’s strategic theory of mind. Proc Natl Acad Sci USA 111(37):13307–13312. 53. Fujii T, Takagishi H, Koizumi M, Okada H (2015) The effect of direct and indirect monitoring on generosity among preschoolers. Sci Rep 5:9025. 54. Takagishi H, et al. (2015) The development of the effect of peer monitoring on generosity differs among elementary school-age boys and girls. Front Psychol 6:895. 55. Benenson JF, Pascoe J, Radmore N (2007) Children’s altruistic behavior in the dictator game. Evol Hum Behav 28(3):168–175. 56. Fehr E, Bernhard H, Rockenbach B (2008) Egalitarianism in young children. Nature 454(7208):1079–1083. 57. Keysar B, Lin S, Barr DJ (2003) Limits on theory of mind use in adults. Cognition 89(1): 25–41. 58. Apperly IA, et al. (2010) Why are there limits on theory of mind use? Evidence from adults’ ability to follow instructions from an ignorant speaker. Q J Exp Psychol (Hove) 63(6):1201–1217. 59. Miller BL, Cummings JL (2007) The Human Frontal Lobes: Functions and Disorders (Guilford, New York). 60. Osaka R, Umemoto T (1984) Revised Kyoto University NX-15: The Second Version of an Intelligence Test (Taisei Shuppan, Tokyo). Japanese.

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7. Morishima Y, Schunk D, Bruhin A, Ruff CC, Fehr E (2012) Linking brain structure and activation in temporoparietal junction to explain the neurobiology of human altruism. Neuron 75(1):73–79. 8. Steinbeis N, Bernhardt BC, Singer T (2012) Impulse control and underlying functions of the left DLPFC mediate age-related and age-independent individual differences in strategic social behavior. Neuron 73(5):1040–1051. 9. Spitzer M, Fischbacher U, Herrnberger B, Grön G, Fehr E (2007) The neural signature of social norm compliance. Neuron 56(1):185–196. 10. Knoch D, Pascual-Leone A, Meyer K, Treyer V, Fehr E (2006) Diminishing reciprocal fairness by disrupting the right prefrontal cortex. Science 314(5800):829–832. 11. Knoch D, et al. (2008) Studying the neurobiology of social interaction with transcranial direct current stimulation–The example of punishing unfairness. Cereb Cortex 18(9):1987–1990. 12. Baumgartner T, Knoch D, Hotz P, Eisenegger C, Fehr E (2011) Dorsolateral and ventromedial prefrontal cortex orchestrate normative choice. Nat Neurosci 14(11): 1468–1474. 13. Ruff CC, Ugazio G, Fehr E (2013) Changing social norm compliance with noninvasive brain stimulation. Science 342(6157):482–484. 14. Zaki J, Mitchell JP (2013) Intuitive prosociality. Curr Dir Psychol Sci 22(2):466–470. 15. Rand DG, Greene JD, Nowak MA (2012) Spontaneous giving and calculated greed. Nature 489(7416):427–430. 16. Rand DG, et al. (2014) Social heuristics shape intuitive cooperation. Nat Commun 5:3677. 17. Yamagishi T, Terai S, Kiyonari T, Mifune N, Kanazawa S (2007) The social exchange heuristic: Managing errors in social exchange. Rationality Soc 19(3):259–291. 18. Cappelletti D, Goth W, Ploner M (2011) Being of two minds: Ultimatum offers under cognitive constraints. J Econ Psychol 32(6):940–950. 19. Cornelissen G, Dewitte S, Warlop L (2011) Are social value orientations expressed automatically? Decision making in the dictator game. Pers Soc Psychol Bull 37(8): 1080–1090. 20. Schulz JF, Fischbacher U, Thöni C, Utikal V (2014) Affect and fairness: Dictator games under cognitive load. J Econ Psychol 41:77–87. 21. 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. Organ Behav Hum Decis Process 83(2):185–212. 22. Lotz S (2015) Spontaneous giving under structural inequality: Intuition promotes cooperation in asymmetric social dilemmas. PLoS One 10(7):e0131562. 23. Rand DG, Kraft-Todd G, Gruber J (2015) The collective benefits of feeling good and letting go: Positive emotion and (dis)inhibition interact to predict cooperative behavior. PLoS One 10(1):e0117426. 24. Tabibnia G, Satpute AB, Lieberman MD (2008) The sunny side of fairness: Preference for fairness activates reward circuitry (and disregarding unfairness activates selfcontrol circuitry). Psychol Sci 19(4):339–347. 25. Camerer C (2003) Behavioral Game Theory (Princeton Univ Press, Princeton). 26. Sanfey AG, Rilling JK, Aronson JA, Nystrom LE, Cohen JD (2003) The neural basis of economic decision-making in the Ultimatum Game. Science 300(5626):1755–1758. 27. Takagishi H, Fujii T, Kameshima S, Koizumi M, Takahashi T (2009) Salivary alphaamylase levels and rejection of unfair offers in the ultimatum game. Neuroendocrinol Lett 30(5):643–646. 28. Dalwani M, et al. (2011) Reduced cortical gray matter volume in male adolescents with substance and conduct problems. Drug Alcohol Depend 118(2-3):295–305. 29. Mostofsky SH, Cooper KL, Kates WR, Denckla MB, Kaufmann WE (2002) Smaller prefrontal and premotor volumes in boys with attention-deficit/hyperactivity disorder. Biol Psychiatry 52(8):785–794. 30. Weygandt M, et al. (2013) The role of neural impulse control mechanisms for dietary success in obesity. Neuroimage 83:669–678. 31. Figner B, et al. (2010) Lateral prefrontal cortex and self-control in intertemporal choice. Nat Neurosci 13(5):538–539. 32. Hare TA, Camerer CF, Rangel A (2009) Self-control in decision-making involves modulation of the vmPFC valuation system. Science 324(5927):646–648.

Supporting Information Yamagishi et al. 10.1073/pnas.1523940113 SI Methods: The Mac Game The Mac game was played by three players—the Money Placer, the Box Chooser, and the Envelope Chooser. All participants played the role of Money Placer (see below) eight times, each time with a different set of parameters (i.e., which envelope of a pair of envelopes contained a 500-yen coin for each of two boxes). A participant who believed that other participants would prefer more money than less money can infer the consequences of placing a 500-yen coin in one of two boxes, in terms of how much he or she will earn. In the example shown on pp. 12–14 of the instructions (Dataset S1), the Box Chooser will choose the blue–red box if the participant places a 500 coin in it as a Money Placer. Then, the Envelope Chooser will choose the red envelope that contains a 500 coin. This will yield 200 yen for the participant when he or she plays the role of Money Placer. If he or she placed a 500-yen coin in the green–yellow box, the Box Chooser will select that box to earn the coin, providing a choice of the green envelope and the yellow envelope to the Envelope Chooser, who will choose the green envelope to earn the 500 yen in it. This choice by the Envelope Chooser will yield 100 yen for the participant. Given the combination of coins and envelopes, the choice for the participant as Money Placer is between receiving 200 yen for placing the 500-yen coin in the blue–red box and 100 yen for

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placing the same coin in the green–yellow box. Assuming all players aim to earn more money rather than less, the strategic choice is to place the 500-yen coin in the green–red box. Any social preferences cannot affect the Money Placer’s choice because the other two members can earn 500 yen each, regardless of the box in which the 500 yen is placed. Similarly, the Money Placer cannot count on the Envelope Chooser’s prosocial preference (the choice of the envelope that yields more money for Money Placer) because the Envelope Chooser does not know what the Money Placer earns for each envelope color. The possible reasons for not choosing the option that maximizes the Money Placer’s earnings are either he or she does not want to earn more money rather than less (or the cognitive cost of thinking is higher than the cost of not earning the highest possible money) or he or she does not realize that this choice affects the other two members’ choices, and ultimately his or her own earnings. As suggested by studies of adult use of theory of mind (see refs. 51–53), having the ability to predict that most people would prefer more money than less money and using this knowledge to maximize one’s own earnings are different processes. Strategic thinking requires the ability to predict others’ choices and the consequences of one’s own choices and the readiness to use the predicted responses of other people to achieve one’s own goal.

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Fig. S1. Superior frontal gyrus in the Destrieu Atlas (A), the relationship of its cortical thickness and strategic choice (UG–DG) (B), the relationship of its right cortical thickness and giving in the UG and DG (C), and the relationship of its left cortical thickness and giving in the UG and DG (D).

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Fig. S2. Superior frontal sulcus in the Destrieu Atlas (A), the relationship of its cortical thickness and strategic choice (UG–DG) (B), the relationship of its right cortical thickness and giving in the UG and DG (C), and the relationship of its left cortical thickness and giving in the UG and DG (D).

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Fig. S3. Middle frontal sulcus in the Destrieu Atlas (A), the relationship of its cortical thickness and strategic choice (UG–DG) (B), the relationship of its right cortical thickness and giving in the UG and DG (C), and the relationship of its left cortical thickness and giving in the UG and DG (D).

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Fig. S4. Inferior frontal sulcus in the Destrieu Atlas (A), the relationship of its cortical thickness and strategic choice (UG–DG) (B), the relationship of its right cortical thickness and giving in the UG and DG (C), and the relationship of its left cortical thickness and giving in the UG and DG (D).

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Fig. S5. Frequency distribution of participants who made zero through eight correct responses in the Mac game and the mean IQ of the respective participants. Error bars are SEs.

Dataset S1. Original instructions for the Mac game in Japanese and their English translations Dataset S1

Dataset S2. Data used for analysis Dataset S2

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次のページから課題の説明が始まります。 説明には音声が付きますのでヘッドフォンを お使いください。

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はじめに この課題は、３人ひと組みで行っていただきます。 作業の内容は３人それぞれ違います。 ３人の決定の組み合わせで、あなたが手に入れる金額と、他の２人 が手に入れる金額が決まります。

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作業内容の説明 ３人は、「箱にお金を入れる人」、「箱を選ぶ人」、「封筒を選ぶ人」 のどれかに割り当てられます。誰がどの作業をするかは、コン ピュータがランダムに決めます。 or or

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or

or

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「箱にお金を入れる人」がすること ステップ①

箱にお金を 入れる人

「箱にお金を入れる人」は、「青・赤の箱」と「緑・黄の箱」 の２つの箱のどちらかに５００円を入れ、どちらかに１００ 円を入れます（このお金は「箱にお金を入れる人」のもの にはなりません）。

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「箱を選ぶ人」がすること ステップ①

箱にお金を 入れる人

ステップ②

＿

箱を選ぶ人

次に、「箱を選ぶ人」は、どちらの箱に５００円、どちらの箱に１００円 が入っているかを知らされます。次に、２つの箱のどちらかを選びま す。 選んだ箱に入っているお金（５００円か１００円）は、「箱を選ぶ人」の ものとなります。

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箱の中には封筒が入っています ステップ②

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それぞれの箱の中には封筒が入っています。「青・赤」の 箱には青色の封筒と赤色の封筒が、「緑・黄」の箱には緑 色の封筒と黄色の封筒が入っています。

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ステップ②

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箱を選ぶ人

そして、青と赤の封筒のどちらかに５００円が、もう一方に １００円が入っています。また、緑と黄色の封筒のどちらか に５００円が、もう一方に１００円が入っています。それぞ れの封筒にいくら入っているかはコンピュータが決めます。

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＿

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「封筒を選ぶ人」は、「箱を選ぶ人」が選んだ箱の２つの封筒のうち、 どちらに５００円が、どちらに１００円が入っているかを知らされます。 その上で、どちらかの封筒を選びます。選んだ封筒の中のお金 （５００円か１００円）は、「封筒を選ぶ人」のものになります。

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「箱にお金を入れる人」が獲得する金額

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「封筒を選ぶ人」が選んだ封筒の色によって、「

」が獲得する金額が決まります。１００円、２０ ０円、３００円、４００円のどれかになります。 「封筒を選ぶ人」が何色を選んだ時に「箱にお金を入れる人」がいくら もらえるかはあらかじめコンピュータが決めます。このお金は封筒の 中のお金から支払われるわけではありません。 コンピュータから直接 支払われます。

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どの封筒がいくらになるかは「箱にお金を入れる人」ごとに違 います（下の例以外にも、いろいろな組み合わせがあります）。

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300円

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「箱にお金を入れる」作業 or

封筒を選ぶ人 の獲得額

箱にお金を 入れる人（あな た）の獲得額

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箱にお金を 入れる人

「箱にお金を入れる人」が作業をするときには、コンピュータ画面上で、 どの封筒にコンピュータがいくら入れたかと、どの封筒が選ばれると 自分がいくらもらえるかを教えてもらえます。

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注意：あとの２人は、どの封筒を選ぶと「箱にお金を入れる人」 がいくらもらえるかは知りません。

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あなたが「箱にお金を入れる」場合の実際の画面の例

例 封筒に入れる金額を、 コンピュータが決め ています

300円

200円

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箱にお金を 入れる人（あなた）

後で説明するように、「箱にお金を入れる」決定は、３人の組み合わ せを変えながら何回か行います。毎回、まず最初に上の画面が出 て、それぞれの封筒に入るお金をコンピュータが決めます。コン ピュータが５００円と１００円を入れる封筒は毎回変わります。

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あなたが「箱にお金を入れる」場合の実際の画面の例

例 封筒に入れる金額を、 コンピュータが決め ています

300円

200円

100円

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箱にお金を 入れる人（あなた）

また、どの封筒が選ばれた時にあなたがいくら獲得できるかも、画 面上に表示されます。 ※先ほども説明したように、あなたが獲得するお金は封筒を選ぶ人 が獲得したお金とは別に、コンピュータから直接支払われます。

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あなたが「お金を入れる」場合の実際の画面の例

例

300円

200円

100円

400円

箱にお金を 入れる人（あなた）

この画面は例なので、今はボタンを押すことはできません

封筒の中に入る金額が決まると、箱の上に「決定ボタン」が表示さ れます。５００円を入れたい箱の上にあるボタンを押してください。 もう一つの箱には、１００円が自動的に入れられます。

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作業全体の流れ ① まず「箱にお金を入れる人」が、どちらの箱に ５００円を入れ、どちらに１００円を入れるかを 決めます。 ② 次に、「箱を選ぶ人」がどちらかの箱を選び、 中に入っているお金（１００円か５００円）を手に 入れます。

or or

③ その次に、「封筒を選ぶ人」がどちらかの封筒 を選び、中に入っているお金（１００円か５００ 円）を手に入れます。 ④ 「箱にお金を入れる人」は、「封筒を選ぶ人」が 選んだ封筒に割り当てられた金額を手に入れ ます。

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300円 200円 100円 400円

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説明の最初に戻る

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追加の説明

・作業は、実際に箱や封筒を使って行うのではなく、すべてコン ピュータ上で行います。 ・作業にあたっては、まず最初に全員が「箱にお金を入れ

る」作業だけを何回か行います。 ・ただし毎回、それぞれの封筒に入っている金額が変わります。

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追加の説明

・全員が何回か「箱にお金を入れる」作業を終えると、どの回の作業 にお金を支払うかをコンピュータが決めます。 ・次にコンピュータが３人の組み合わせを決めて、そのうちの１人を 本当に「箱にお金を入れる人」に選びます。 ・「箱にお金を入れる人」に選ばれなかった残りの２人を、コンピュー タが、「箱を選ぶ人」か「封筒を選ぶ人」に割り当てます。

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獲得（かくとく）金額についてのまとめ ・「箱を選ぶ人」に割り当てられた場合には、自分が選ん だ箱に入っている金額を手に入れます。 ・「封筒を選ぶ人」に割り当てられた場合には、自分が選 んだ封筒に入っている金額を手に入れます。 ・「箱にお金を入れる人」に割り当てられた場合には、「封 筒を選ぶ人」が選んだ封筒の色に対応する金額を手に 入れます。 ・あなたが獲得（かくとく）した金額は、本日の謝礼に加え られます。

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これで実験の説明は終わりです

参加者全員の準備がととのうまで しばらくお待ちください。

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Instructions for this task will start from the next page. Please use the headphones for the voice instructions. Press the button below when you are ready. Start Instructions

Introduction

This task will be conducted in a group of three participants.

Each of the three participants will perform a different task.

How much you and the other two participants earn will depend on the combination of the three people’s decisions.

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Instructions for the task

Each of the 3 members of your group will be assigned to the role of either “Money Placer”, “Box Chooser,” or “Envelope Chooser.” Who is assigned to which role will be determined by the computer. or or

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Step ①

“Money Placer’s” Task Money Placer

“Money Placer” places a 500 yen coin in one of the two boxes, the Blue-Red box or the Green-Yellow box, and a 100 yen coin in the other box. (The Money Placer will not earn the money.)

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Step ①

Step ②

“Box Chooser’s” Task Money Placer

Box Chooser

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The Box Chooser is then informed which box contains a 500 yen coin and which contains a 100 yen coin, and is asked to choose a box. The coin contained in the box will become the Box Chooser’s.

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Note: the hand turns while the participant reads the instruction

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Each box contains two envelopes Step ②

Box Chooser

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In addition to a coin, each box contains two envelopes. The blue-red box contains a blue envelope and a red envelope. The green-yellow box contains a green envelope and a yellow envelope.

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Step ② Box Chooser

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Either the blue or the red envelope contains a 500 yen coin and the other a 100 yen coin. Similarly, either the green envelope or the yellow envelope contains a 500 yen coin and the other a 100 yen coin. The computer has decided which envelope contains which coin.

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“Envelope Chooser’s Task

Step ②

Box Chooser

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Step ③ Box Chooser

Envelope Chooser has been informed which envelopes contain a 500 yen coin, and which contains 100 yen coin, and chooses one of the two envelopes in the box chosen by Box Chooser. Envelope Chooser earns the coin (500 yen or 100 yen) in the envelope he or she has chosen.

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Note: the hand turns while the participant reads the instruction

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How much Money Placer will earn

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How much Money Placer earns will depend on Money Placer

the color of the envelope that Envelope Chooser has chosen. Money Placer will earn either 100, 200, 300 or 400 yen. The computer has already determined how much Money Placer

earns when a particular color envelope is chosen by Envelope Chooser. The money for Money Placer does not come from the money (coin) that the Envelope Chooser has chosen. The computer will directly pay.

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The color-earnings combination varies from participant to participant.

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The Task of Placing Coins in the Boxes or

Envelope Chooser’s earnings

Money Placer

Money Placer’s (Your) earnings

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Money Placer will know which envelope contain a 500 yen coin and which contain a 100 yen coin for Envelope Chooser. Money Placer also knowd which envelope is worth how much for him or her.

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Note：The other two will not know how much each envelope is worth for Money Chooser.

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Money Placer’s Screen Display You Will See When You Play the Role

Example The computer is determining which envelope contains which coin.

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Money Placer Note on animation: First, the text “The computer …” flashes and the (You) coins turns between 500 and 100. Then the text says “The computer

has determined which envelopes contain a 500 yen coin and which contain a 100 yen coin.” The screen then displays which coins are 500 yen coins and which are 100 yen coins.

You will make this decision several times, each time in a different group of three participants. Each time, you will see a screen like this, and will be asked to decide in which box to place a 500 yen coin.

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Money Placer’s Screen Display You Will See When You Play the Role

Example The computer is 封筒に入れる金額を、 determining which コンピュータが決め envelope contains ています which coin.

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Money Placer (You)

Also, you will be shown how much you will earn as Money Placer depending on the color of the envelope chosen by Envelope Chooser. * As explained earlier, the money you earns is paid to you directly by the computer independent of the money Envelope Chooser earns.

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Money Placer’s Screen Display You Will See When You Play the Role

Example The computer has placed coins in the envelopes. Please decide which box to place a 500 yen coin.

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Money Placer (You)

You cannot press a button on this example screen.

When the computer has determined in which envelopes to place 500 yen and 100 yen coins, the decision button will appear. Please decide in which box to place a 500 yen coin, and press the corresponding button. A 100 yen coin will be automatically placed in the other box.

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Overall flow of the task

① First, Money Placer determines in which box to place a 500 yen coin.

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② Next, Box Chooser chooses a box and earns the money (500 yen coin or 100 yen coin) placed in the chosen box.

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③ Then, Envelope Chooser chooses an envelope and earns the coin (500 yen or 100 yen) that the computer has placed in it. ④ Money Placer earns the money predetermined by the computer for the color of the envelope chosen by Envelope Chooser.

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The instructions for the task are over. If you want to read the instructions from the beginning, press the button below. If you understand the instructions well, press the right arrow and proceed to the next page.

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Return to the First Page

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Additional Instructions ・You will not use the actual boxes and envelopes. You will perform the task on the computer screen. ・Everyone will first perform the “money placing task” several times. ・Each time which coin is entered by the computer to which envelope will change.

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Additional Instructions ・When all participants have finished the several rounds of money placing task, the computer picks a round for actual payment.

・Then, the computer forms three-person groups, and assigns the role of Money Place to one of the three members of each group.

・The remaining two members in each group will be assigned the role of either Box Chooser or Envelope Chooser.

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Summary of how to earn money

・If you are assigned the role of Box Chooser, you will earn the coin that is contained in the box you have chosen. ・If you are assigned the role of Envelope Chooser, you will earn the coin that is contained in the envelope you have chosen.

・If you are assigned the role of Money Placer, you will earn the amount of money predetermined by the computer for the color of the envelope chosen by Envelope Chooser. ・The amount of money you have earned will be added to your total earnings for today.

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This is the end of the instructions. Please call the experimenter by pressing the call button.

If you have any questions, please feel free to ask the experimenter. Please wait until all participants finish reading the instructions.

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