Costless and Costly Prosociality: Correspondence Among Personality Traits,Economic Preferences,and Real-World Prosociality

Abstract

Prosociality can either be costly (e.g., donating to charity) or costless (e.g., posthumous organ donation). Whereas links between personality and costly prosociality have been explored, links with costless prosociality and personality are at present unknown. We address this in two studies: Study 1 (N = 200) confirms the distinction between costless and costly prosociality based on willingness to engage with health and nonhealth prosociality. Study 2, using data from four samples (student and community; N = 733) shows, across incentivized and hypothetical economic games to assess costless (generosity game [GG]) and costly (dictator game [DG]) prosociality, that organ donor behavior was linked to greater allocations in the GG and that charity/volunteering behavior was linked to greater allocations in the DG. Costless and costly prosocialities are associated with different personality traits (e.g., costly with politeness and compassion and costless with intellect). Implications for cooperative phenotypes and recruiting organ donors are discussed.

Keywords

altruism costly personality organ donation dictator game

Perceived cost is a key determinant of helping (Stewart-Williams, 2007) with helping generally decreasing with increasing costs (Bode, Miller, O’Gorman, & Codling, 2015). Studies using economic games or examining real-world prosociality have typically focused on costly giving such as dictator game (DG) allocations, volunteering, and charitable donations (Bekkers & Wiepking, 2010; Böckler, Tusche, & Singer, 2016). In all of these instances, the individual must bear a considerable cost in terms of time, money, or effort in order to assist or benefit another person. However, while the vast majority of research into prosociality has focused on costly helping, there are prosocial acts that can be considered extremely low cost and thus relatively costless and in some case zero-cost such as posthumous organ donation (Bekkers, 2006; Moorlock, Ives, & Draper, 2014). Thus, just considering costly helping, in isolation, does not provide a complete coverage of the prosocial domain. In the present studies, therefore, we examine the distinction between costless and costly prosociality. We firstly explore the factor structure of people’s willingness to engage in general costless and costly prosociality and then examine the correspondence that costless and costly prosocial preferences in lab-based economic games have with real-world prosocial behavior and personality traits.

Prosociality and Cost

Some prosocial behaviors are costly because they consume resources that become depleted by giving, whereas other prosocial acts are very low-cost/relatively costless, in that the giver has sufficient resources to expend without detriment (Zahavi & Zahavi, 1997). Still, other prosocial acts may be zero-cost as the giver no longer needs the resource (Moorlock et al., 2014; Shepherd, O’Carroll, & Ferguson, 2014).

Examples of real-world costly prosocial acts include charitable giving (which involves sacrificing money) and volunteering (which involves sacrificing time) for the benefit of others (Böckler et al., 2016). In the lab, costly prosocial preferences can be assessed using the dictator game (DG; Forsythe, Horowitz, Savin, & Sefton, 1994), where one player (the dictator) decides how to split a fixed amount of money—usually with an anonymous recipient—who must accept this unconditionally (Forsythe et al., 1994). Given the constant-sum nature of the game, the dictator has to bear a cost to be prosocial to the recipient.

A classic example of costless real-world prosociality is posthumous organ donation (Moorlock et al., 2014; Shepherd et al., 2014). Religious or spiritual concerns aside, this act is ultimately costless because the donor bears no cost at the time of deciding to donate and, once deceased, is no longer in need of their organs. Within behavioral economics, costless prosocial preferences can be explored using the generosity game (GG; Güth, 2010; Güth, Levati, & Ploner, 2012; Zhao, Ferguson, & Smillie, 2016). Here, one player (the proposer) has a fixed amount of money (e.g., US$5) to keep and must decide how much of a given range of money (e.g., US$0–10) another player should receive (see Güth et al., 2012). Because the proposer’s own windfall is fixed, the cost of allocating to the recipient is zero—the proposer will leave with US$5 regardless of what they allocate to the recipient. Research shows that most proposers choose to maximize the recipient’s payoff, while a substantial portion prefer an equal share to the recipient (Güth, 2010; Güth et al., 2012).

Aims of the Current Article

The main aim of this article is to demonstrate that costless and costly prosociality are distinct factors of prosociality. This adds to the existing research that has focused solely on the factor structure of costly prosociality (Böckler et al., 2016; Hubard, Harbaugh, Srivastava, Degras, & Mayr, 2016; Peysakhovich, Nowak, & Rand, 2014). We address this aim by (1) exploring the factor structure of people’s willingness to engage in a variety of costless and costly prosociality (Study 1), (2) exploring the correspondence between costless and costly real-world and lab-based prosociality (Study 2), and (3) examining how these prosocialities are linked with personality (Study 2).

Study 1: Costless Versus Costly Prosociality in the Context of Health Versus Nonhealth Behaviors

To study the (zero) costless–costly prosociality distinction, we explore the factor structure for costless and costly health and nonhealth-based prosociality. Bekkers (2006) argues that health and nonhealth-based prosociality are distinct and should be assessed separately. Thus, we identify costless and costly aspects of both health and nonhealth prosociality. For example, costless nonhealth prosociality can be seen in behaviors such as donating unwanted clothes to charity and signing a petition, whereas instances of costly nonhealth prosociality include donating money to charity. Costly health prosociality is seen in behaviors like blood donation (Lyle, Smith, & Sullivan, 2009), whereas costless health prosociality is seen in posthumous organ donation (Shepherd et al., 2014). Thus, we cross costless versus costly with health versus nonhealth prosociality to examine whether the costless–costly distinction is identifiable.

Method

Participants

Two hundred participants (mean age = 24.6, SD = 3.4; 50% male) recruited across a UK university campus took part, and the sample size is sufficient given the number of items to produce a stable factor structure (Ferguson & Cox, 1993).

Measures

As part of a larger study on motivations and prosociality, participants indicated the extent to which they would be willing (from 1 = not at all to 5 = very likely) to perform each of 12 behaviors selected to assess archetypal costly and costless prosociality for both health and nonhealth prosocial acts (see Table 1 and Supplementary Files Text S1 and Supplementary Tables S1, S2 and, S3 for the rationale for the behaviors included and excluded).

Table 1.
CFA Factor Loadings and Latent Factor Intercorrelations for Model 6.

Behaviors Factors

1 2 3 4

Donate your unwanted clothes to charity .913 .000 .000 .000

Give someone a concert ticket for free that you can no longer use .554 .000 .000 .000

Donate any unwanted toys to charity .690 .000 .000 .000

Donating 20% of your salary to charity .000 .617 .000 .000

Donate money to help those you think have unfairly lost their jobs .000 .780 .000 .000

Volunteer your time regularly to help out at a local charity shop .000 .377 .000 .000

Donate blood once .000 .000 .806 .000

Donate blood regularly .000 .000 .831 .000

Give someone your car parking ticket, for free, when there is still time left on it .000 .000 .000 .604

Register as an organ donor to donate organs after your death .000 .000 .000 .553

Sign a petition .000 .000 .000 .658

Vote in a general election .000 .000 .000 .672

Correlations across latent factors

Factor 1: Costless nonhealth prosociality 1

Factor 2: Costly nonhealth prosociality .381 1

Factor 3: Costly health prosociality: blood donation .278 .116 1

Factor 4: Costless communal and civic duty .521 −.063 .415 1

Note. Coefficients in boldface indicate significant loadings and significant intercorrelations. CFA = confirmatory factor analysis.

Analysis

We specified a series of confirmatory factor analyses (CFAs) in Mplus 7, using weighted least squares with mean and variance adjustment (WLSMV) to account for the ordinal nature of these data. We specified (i) a one-factor prosociality model (Model 1); (ii) a two-factor model with correlated costly and costless factors (Model 2); (iii) a two-factor model with correlated health and nonhealth factors (Model 3); (iv) a four-factor model with orthogonal costly, costless, health, and nonhealth factors (Model 4); and (v) a four-factor model with the costly and costless factors correlated and the health and nonhealth factors correlated and all other associations orthogonal (Model 5; see Supplementary Text S1 and Tables S2 and S3 and S3 for items and their factor targets).

Results and Discussion

Fit statistics for the CFA models are presented in Table 2. While Model 5 is the best fit to these data, the pattern of factor loadings suggested a different modified model such that blood donation represented a high-cost prosociality factor and that low-cost prosociality is represented by two factors: (1) “communal and civic duty” (e.g., signing a petition, voting, registered organ donor, and giving away a car parking ticket) and (2) “generic nonhealth costless” prosociality (e.g., giving unwanted clothes or toys to charity or someone a free concert ticket; see Supplementary Text S2 and Table S4 for more detail and the rationale for the revised model). This modified model was specified and is referred to as Model 6 in Table 1. This model is both a good fit to these data with an interpretable structure (Table 1).

Table 2.
CFA Model Fit Statistics.

Models χ² (df) CFI TLI RMSEA

Model 1: One factor 311.06 (54)* .59 .49 .15*

Model 2: Two factors (costly vs. costless) 255.85 (53)* .72 .65 .13*

Model 3: Two factors (health vs. nonhealth) 226.70 (53)* .72 .65 .13*

Model 4: Four factors (health, nonhealth, costly, and costless)—all orthogonal 106.14 (42)* .90 .84 .09*

Model 5: Four-factor (health vs. nonhealth, costly vs. costless)—health versus nonhealth, costly versus costless—oblique 66.41 (40)* .96 .93 .06

Modified model

Model 6: Four-factor oblique—costless (generic or communal/civic) and costly (health or nonhealth) 74.97 (48) .96 .94 .05

Note. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; CFA = confirmatory factor analysis.

p < .01. p < 001.

In Model 6, there are two types of costless prosociality, one focused on communal and civic duty (Factor 4) that includes organ donation, the other being a generic nonhealth costless prosociality (Factor 1). Similarly, costly prosociality is split into health (Factor 3), focusing on blood donation, versus general nonhealth behaviors (Factor 2). We conducted a sensitivity analysis that showed that this factor structure was not influenced by our strict item exclusion criteria concerning living organ donation (Supplementary Text S3 and Table S5).

Study 2: Correspondence Between Real-World and Lab-Based Prosociality

Study 1 demonstrates a clear distinction between costless and costly prosociality. Here, we move beyond self-reports and examine the correspondence between an archetypal costless behavior—posthumous organ donation—and allocations in the GG (a costless behavioral preference). We also examine the correspondence between archetypal costly behaviors—donating to charity and volunteering—and allocations in the DG (a costly behavioral preference). We choose to explore organ donation as an example of costless prosociality as it is endorsed by a large number within the population, whereas numbers of people giving unwanted clothes to charity, for example, is not clearly measured or known (U.S. Department of Health and Human Services, 2016). Furthermore, organ donation reflects aspects of nonhealth costless prosociality (see Table 1) and thus has generalizability beyond the health domain.

Our predictions are based on self-perception* theory from psychology (Bem, 1972; Baumeister, 1998) and self-image models from economics (Benabou & Tirole, 2006; see also Tonin & Vlassopoulos, 2013). These suggest that people’s prosocial behaviors reflect, to an extent, their underlying values and people act in a manner consistent with these (see also Yamagishi et al., 2009). Thus, we predict that those who identify as an organ donor will allocate more to a partner in the GG (but not the DG), as this will be consistent with them being a generous person who gives when there are no real costs. On the other hand, those who engage in donating to charity and volunteering will allocate more to a partner when it comes to costly giving in the DG (but not the GG) as again this is more consistent with their self-image as a person who gives when it is costly.

Links With Personality

There is a growing literature showing how personality traits predict giving in the real-world and prosocial preferences within economic games (Zhao & Smillie, 2015). Based on this literature, we offer a number of predictions about how costless and costly prosociality will differ with respect to the aspects of the Five Factor Model of personality. This consists of five broad trait domains (i.e., neuroticism, agreeableness, conscientiousness, extraversion, and openness/intellect), each of which subsumes two narrower traits (known as aspects) which reflect distinct but correlated tendencies (DeYoung, Quilty, & Peterson, 2007; DeYoung, Weisberg, Quilty, & Peterson, 2013). The five-factor domains and their aspects are detailed in Table 3. Examining the trait predictors of costly versus costless prosociality at this fine-grained level of personality may shed light on some of the psychological mechanisms underpinning each different form of prosociality.

Table 3.
Aspects of the Big Five Model of Personality, From the Big Five Aspect Scales.

Trait (Aspect) Description Example Item α

Neuroticism

Withdrawal Tendency to experience anxious and depressive traits Seldom feel blue .88

Volatility Tendency to be irritated, angry, and emotionally labile Get angry easily .90

Agreeableness

Politeness Tendency to respect others and adhere to social norms Hate to seem pushy .78

Compassion Tendency to be emotionally concerned about others’ well-being Sympathize with others’ feelings .90

Conscientiousness

Industriousness Tendency to be focused on goal pursuit and carry out plans Finish what I start .87

Orderliness Tendency to be rule-abiding and organized Follow a schedule .81

Extraversion

Enthusiasm Tendency to be gregarious and to experience positive emotions Warm up quickly to others .88

Assertiveness Tendency to be bold and dominant Take charge .91

Openness/intellect

Openness Tendency for engagement with perceptual and aesthetic domains Enjoy the beauty of nature .82

Intellect Tendency for intellectual curiosity and engagement Like to solve complex problems .88

Note. N = 733. Adapted from DeYoung, Quilty, and Peterson (2007). Cronbach’s α is calculated from the combined samples of the current study.

For example, previous research indicates that allocations in the standard DG (costly helping) are predicted by politeness (Zhao, Ferguson & Smillie, 2017a). However, both politeness and compassion (the tendency to be emotionally concerned for others) aspects of agreeableness are linked to DG allocations when these are framed within real-world contexts of need and equity (Zhao, Ferguson, & Smillie, 2017b). In accordance with this, we expect that indices of costly prosociality should be linked to both politeness and compassion (Zhao et al., 2016; Zhao et al., 2017a). Conscientiousness has also been linked to costly effortful real-world prosociality in terms of predicting the frequency of repeat blood donation (Ferguson, 2004). Thus, we expect that the industriousness aspect of conscientiousness, as this specifically reflects effort in pursuit of a goal, should be positively linked to costly prosociality. On the other hand, as costless prosociality reflects no real cost to the individual, it may be driven more by norm adherence (i.e., to “do good”) and linked, therefore, more to politeness than compassion. However, at present, there are no data on which to make specific predictions regarding costless prosociality and as such this is a more exploratory aspect of the current article and should, therefore, add new and novel findings to the literature.

Method

Samples

We tested our predictions across four samples drawn from both predominantly Australian student (Samples 1 and 3, hereafter referred to as “students”) and U.S. community (Samples 2 and 4, hereafter referred to as Mechanical Turk [MTurk]) populations. Australian students were recruited from online advertisements and flyers posted around the University of Melbourne, Australia, and completed the study for monetary payment or course credit. U.S. community members (U.S. residents) were recruited from the online marketplace, Amazon MTurk, and completed the study online for monetary payment. The final overall N was 733. The mean age of the samples ranged from 19.63 (Sample 3) to 34.76 (Sample 4) years. Table 4 presents further details of each sample. This greatly exceeds our minimum target sample size of at least 175 participants, which provides 80% power to identify an effect sizes of r = .21 (Faul, Erdfelder, Buchner, & Lang, 2009), which is in line with previous findings for the role of agreeableness in DGs (Zhao et al., 2017a). The greater sample size allows us to control for sample differences and to explore the role of sex and different game structures (e.g., incentivization).

Table 4.
Summary of Samples in Study 2.

Sample 1 Sample 2 Sample 3 Sample 4

Descriptors N = 70 N = 304 N = 103 N = 256

Number (%) removed after comprehension and/or attention checks 33 (32%); four comprehension checks of games and one of two attention checks embedded within personality measures 26 (7.9%); two attention checks embedded within personality measures 19 (15.6%); two attention checks embedded within personality measures 10 (3.8%); two attention checks embedded within personality measures

Participants University students and community members in Australia U.S. Amazon MTurk workers (with fewer than 50 Human Intelligence Tasks) First-year psychology students at an Australian university U.S. Amazon MTurk workers

Age 18–33 years (M _age = 22.34, SD = 3.76) 18–65 years (M _age = 30.90, SD = 9.89) 18–47 years (M _age = 19.63, SD = 3.71) 19–67 years (M _age = 34.76, SD = 11.00)

Female (%) 65.7 54.9 75.7 42.6

Organ donor (%) 25.7 66.8 29.1 53.5

Administration of study Online Qualtrics survey Online Qualtrics survey Online Qualtrics survey Online Qualtrics survey

Median study time 64 min 30 min 43 min 42 min

Show-up fee AUD$ 15.00 US$2.00 Course credit US$8.00

Game format Full description of dictator and generosity game Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions

Incentivization Incentivized: participants played a number of economic games in which they were matched with another player, and one of these games was selected for payment at the rate of 1 point = AUD$1.00 Hypothetical: participants asked to imagine that they were playing with a stranger that they would not knowingly meet Hypothetical: participants asked to imagine that they were playing with a stranger that they would not knowingly meet Incentivized: participants played a number of economic games in which they were matched with another player, and one of these games was selected for payment at the rate of 1 point = US$0.10

Economic games in study Dictator game, ultimatum game (proposer), ultimatum game (responder), and generosity game. Participants were randomly assigned to one of four different orders of these games based on a Latin squares design.The order of the games was not associated with responses in the dictator and generosity games. Real-world dictator game, dictator game, and generosity game (the latter two were randomized).Experimental condition for the real-world dictator game was not associated with responses in the later dictator and generosity games. Giving/taking framed dictator game, social mindfulness task, dictator game, and generosity game (the latter two were randomized).Giving/taking framing of the earlier dictator game was not associated with responses in the later dictator and generosity games. Giving/taking framed dictator game, dictator game, followed by generosity game.Giving/taking framing of the earlier dictator game was not associated with responses in the later dictator and generosity games.

Note. MTurk = Mechanical Turk.

Materials

DG

In the DG, participants indicated their preferred choice of 11 different payoff combinations that varied in one monetary unit (MU) increments (1 MU = 1 AUD$ in Sample 1, 1 MU = US$0.10 in Sample 4, and 1 MU = 1 hypothetical dollar in Samples 2 and 3). For example, in Sample 1, the 11 different payoff combinations ranged from AUD$0 for oneself and US$10 for one’s partner (scored 10) to US$10 for oneself and US$0 for one’s partner (scored 0), varying in US$1 increments. While stake sizes vary, the evidence shows that this has no systematic effect on behavioral responses (Raihani, Mace, & Lamba, 2013).

GG

In the GG (Güth, 2010; Güth et al., 2012), participants were again asked to indicate their preferred selection of 11 different payoff combinations. These involved the same MUs and conversion rates as those in the DG. This time, their own payoff was always fixed at 5 MUs and the choices ranged from 0 MU (scored 0) to 10 MUs (scored 10) for their partner, varying in 1 MU increments. Although this game was based on the original paradigms of the same name developed by Güth, Levati, and Ploner (2012) and Güth (2010), there is one crucial difference. Participants directly selected their partner’s payoffs (e.g., 0–10 MUs) rather than the total size of the combined payoffs, which was implemented to allow comparability with the format of the DG responses (see Supplementary Text S4 for full instructions for the DG and GG).

Incentivized and hypothetical versions

As there is some evidence that incentivized economic games magnify trait effects in prosocial behavior (Zhao et al., 2016), we administered the games both as hypothetical scenarios with imagined partners (Samples 2 and 3) and as incentivized games with real partners and stakes (Samples 1 and 4). In the incentivized games, participants were informed that their decisions would be matched to another participant and that their earnings from one of the games would be selected for payment at the end of the session. Game payoffs were represented by points that corresponded with real dollar amounts that were paid at the end of the study using participants’ anonymous response identification codes (see Table 4 for details). Participants playing hypothetical versions of these games were asked to imagine that they were playing the games with an anonymous partner who was described as another participant that they would not knowingly meet.

Expressed Real-World Prosociality

To measure costless real-world prosociality, we asked all participants: “are you an organ donor?” (yes/no). This was adapted from questions used to assess blood donor behavior (Ferguson, Taylor, Keatley, Flynn, & Lawrence, 2012).

To measure costly real-world prosociality, we asked and summed responses to two questions: (1) How often have you donated to charity in the past year? and (2) How often have you been involved in any form of volunteer work in the past year? Both were responded to using a 5-point Likert-type scale (1 = 0 times, 2 = 1–2 times, 3 = 3–5 times, 4 = 6–10 times, and 5 = more than 10 times; interitem correlation = .34). These were also adapted from previous work concerning general charity/volunteer identity (Ferguson et al., 2012).

Personality Measure: Big Five Aspect Scales (BFAS; DeYoung et al., 2007)

Participants completed the 100-item BFAS, a widely used and well-validated measure of the five broad domains of personality and each of their two lower level aspects. These were each measured with 10 items per aspect, to which participants responded using a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree). Table 3 provides the α coefficients, indicating that the scales were all reliable. Data on the HEXACO model were also collected but not analyzed here as our hypotheses derive directly from previous work on the BFAS.

Procedure

All participants completed the DG and GG as part of a larger set of economic games (see Table 4; the order and number of games completed did not affect performance on either the DGs and GGs), and also indicated whether or not they were an organ donor, as well as the extent to which they had donated to charity and volunteered.

Statistical Analysis

DG and GG responses were not skewed, but response options were left and right censored. Therefore, we applied ordinary least squares (OLS) as well as Tobit models to account for the left and right censoring. We explored for consensus across analytic strategies to ensure findings were not sensitive to the nature of the DG and GG distributions. As these games show consistent sex effects (Andreoni & Vesterlund, 2001), sex was also included in all models. We initially conducted our analyses aggregating these data across all four samples, and sample (student vs. MTurk) and incentivization (incentivized vs. hypothetical) dummies, as well as their interaction, were included to control for sample differences. To control for any consistent prosocial preference across DG and GG allocations, DG allocations were included as a covariate in the GG model and vice versa.

Results and Discussion

Table 4 indicates the percentage of participants who expressed being an organ donor, with 66.8% and 53.5% in the U.S. MTurk samples and 25.7% and 29.1% in Australian student samples. At present, 54% of the U.S. population have registered as an organ donor (U.S. Department of Health and Human Services, 2016) with a corresponding percentage of 22% in Australia who have registered their intent to donate (Department of Human Services, 2017). Thus, the figures reported in the samples are generally consistent with their nationally representative figures.

Tables 5 and 6 show the regression models for the aggregated data for the GG and DG, respectively. Organ donors versus nondonors allocated more to their partner in the GG (means: 7.0 MUs to the partner vs. 6.2 MUs to the partner; Tobit regression: B = 1.45, 95% confidence interval [CI] = [0.76, 2.12], t = 4.17, p = .000; Table 5), but organ donor status was unrelated to DG allocations (means: 4.0 MUs to the partner vs. 3.8 MUs to the partner; Tobit regression: B = −0.06, 95% CI [−0.40, 0.27] t = −0.36, p = .722; Table 6). Conversely, expressed levels of charity/volunteer prosociality were significantly associated with DG allocations (Tobit regression: B = 0.11, 95% CI [0.03, 0.19], t = 2.82, p = .005; Table 6) but showed no significant association with GG allocations (Tobit regression: B = 0.09, 95% CI [−0.07, 0.25], t = 1.09, p = .277; Table 5). While this pattern is generally seen across all four samples, there are a few variations that are discussed in Supplementary Text S5 and Tables S6 and S7.

Table 5.
Regression Models Predicting Generosity Game Allocations.

Predictor ρ OLS 95% CI Tobit 95% CI

Organ donation (0 = no, 1 = yes) .15* 0.85* [0.44, 1.26] 1.45*** [0.76, 2.12]

Charity/volunteer .03 0.05 [−0.04, 0.15] 0.09 [−0.07, 0.25]

Sex (1 = male, 2 = female) −.19* −0.93* [−1.33, −0.52] −1.58*** [−2.26, −0.91]

Incentives (1 = incentivized, 2 = hypothetical) −.16* −1.69* [−2.55, −0.84] −2.79*** [−4.23, −1.34]

Sample (0 = student, 1 = MTurk) .06 −2.00* [−3.56, −0.44] −1.42* [−2.69, −0.125]

Dictator game −.11** −0.02 [−0.13, 0.09] 0.09 [−0.28, 0.08]

Generosity game

Incentives × Sample 1.21* [0.27, 2.14] 1.97* [0.39, 3.55]

R ² .08*** .02 [0.76, 2.12]

N 729–732 728 728

Note. ρ = Spearman’s ρ; OLS = ordinary least squares; CI = confidence interval; MTurk = Mechanical Turk.

p < .05. p < .01. p < .001.

Table 6.
Regression Models Predicting Dictator Game Allocations.

Predictor ρ OLS 95% CI Tobit 95% CI

Organ donation (0 = no, 1 = yes) .05 −0.06 [−0.34, 0.22] −0.06 [−0.40, 0.27]

Charity/volunteer .13 0.10 [0.03, 0.16] 0.11** [0.03, 0.19]

Sex (1 = male, 2 = female) .14** 0.33* [0.06, 0.61] 0.41* [0.07, 0.74]

Incentives (1 = incentivized, 2 = hypothetical) .40* 2.32* [1.76, 2.88] 2.69*** [2.01, 3.38]

Sample (0 = student, 1 = MTurk) .05 1.94 [0.88, 2.99] 1.04 [0.43, 1.65]

Dictator game

Generosity game −.11 −0.01 [−0.06, 0.04] −0.01 [−0.07, 0.04]

Incentives × Sample −1.02 [−1.65, −0.38] −1.13 [−1.91, −0.36]

R ² .19* .05

N 729–732 728 728

Note. ρ = Spearman’s ρ; OLS = ordinary least squares; CI = confidence interval; MTurk = Mechanical Turk.

p < .05. p < .01. p < .001.

Structure of costless and costly prosociality

To replicate the findings from Study 1, we explore if indices for costly and costless prosociality (both preferences and expressed real-world prosociality) load on distinct components. We ran a CFA where, to reflect their distributions, we specified the DG allocations as censored on lower values and the GG allocations on higher values, the remaining variables were specified as categorical and we used a WLSMV estimator. We also specified a complex survey design and clustered within samples. The CFA fits were excellent, χ² = 4.45 (df = 4), p = .35; comparative fit index (CFI) = .99, Tucker–Lewis index (TLI) = .98, root mean square error of approximation (RMSEA) = .01. The analysis confirms a costly and costless two-factor structure (see Table 7).

Table 7.
CFA for Costless and Costly Prosociality.

Prosociality Measures Costly Helping Costless Helping

Charity .99 .00

Volunteering .39 .00

Dictator game .16 .00

Generosity game .00 .76

Organ donation .00 .26

Latent correlations

1

.26 1

Note. Coefficients in boldface indicate the significant associations and loadings. CFA = confirmatory factor analysis.

Relations with personality

We summed the two factors for costly and costless prosociality and regressed (OLS) these onto the 10 aspects of personality, controlling for sex, incentivization (incentivized game vs. hypothetical scenario), sample type (student vs. MTurk), and the incentivization by sample interaction (Table 8). These results show that costly prosociality is positively associated with the politeness (β = .13; p = .009) and compassion (β = .18; p = .001) aspects of agreeableness, and the assertiveness aspect of extraversion (β = .15; p = .010). Conversely, costless helping was positively associated with the politeness (β = .14; p = .004) aspect of agreeableness and the intellect aspect of openness/intellect (β = .25; p = .009) and negatively associated with the industriousness aspect of conscientiousness (β = −.15; p = .017). Both costly and costless prosociality are, therefore, related to good manners and following social norms. However, costly and costless prosociality can be differentiated in that the former involves empathy, compassion, and social boldness, whereas the latter involves greater intellectual engagement and reduced behavioral effort.

Table 8.
Ordinary Least Squares Regression for Costless and Costly Prosociality.

Predictor Costly Costless

β B 95% CI β B 95% CI

N: Withdrawal −.10 −0.37 [−0.83, 0.09] −.06 −0.22 [−0.67, 0.24]

N: Volatility .02 0.09 [−0.31, 0.49] −.01 −0.05 [−0.45, 0.35]

A: Compassion .18 0.85 [0.37, 1.33] −.01 −0.06 [−0.53, 0.42]

A: Politeness .13* 0.68* [0.17, 1.19] .14 0.74 [0.24, 1.24]

C: Industriousness −.05 −0.23 [−0.74, 0.28] −.15* −0.61* [−1.12, −0.11]

C: Orderliness .03 0.16 [−0.25, 0.57] −.04 −0.17 [−0.57, 0.23]

E: Enthusiasm −.08 −0.34 [−0.76, 0.08] .01 0.05 [−0.36, 0.46]

E: Assertiveness .15 0.58 [0.14, 1.02] −.08 −0.30 [−0.73, 0.13]

O: Intellect −.01 −0.01 [−0.45, 0.44] .25* 1.07* [0.63, 1.51]

O: Openness .01 0.04 [−0.36, 0.45] −.08 −0.39 [−0.78, 0.01]

Gender (1 = male, 2= female) .13* 0.76* [0.31, 1.21] −.14* −0.83* [−1.28, −0.38]

Incentives ( 1 = incentivized, 2 = hypothetical) .24* 1.45* [0.58, 2.31] −.32* −1.87* [−2.72, −1.03]

Sample (0 = student, 1 = MTurk) .05 0.38 [−1.25, 2.01] −.30* −2.00* [−3.61, −0.40]

Incentives × Sample −.04 −0.14 [−1.13, 0.85] .40 1.47 [0.50, 2.44]

N 728 729

R ² .17 .12

Note. B coefficients are unstandardized and β standardized. N = neuroticism; A = agreeableness; C = conscientiousness; E = extraversion; O = openness/intellect; CI = confidence interval.

p < .05. p < .01. p < .001.

Effects of sex, incentives, and sample

The analysis also revealed a number of interesting effects for sex, incentivization, and sample type (i.e., women showed greater costly prosociality than men, while this was reversed for costless prosociality; incentivization increased costless prosociality but reduced costly prosociality). As these were not the focus of this study, they are detailed in the Supplementary Materials (Online Supplementary Text S6, Figure S1) for the interested reader.

General Discussion

Taken together, the present studies yielded a clear, important, and novel finding: Costly prosociality can be distinguished from costless prosociality in both lab-based economic games and real-world prosocial behaviors, and these two forms of prosociality show diverging relations with personality characteristics. Thus, considering only costly prosociality, in isolation, does not provide a complete analysis of the prosocial domain.

Distinguishing Costly and Costless Prosociality

While Böckler et al. (2016) report a multidimensional structure for prosociality, based on both self-report and behavioral data, there is growing evidence, based on behavioral (Brocklebank, Lewis, & Bates, 2011; Peysakhovich et al., 2014; Yamagishi et al., 2013) as well as a mixture of self-report and behavioral data (Hubard et al., 2016), supporting the existence of a general prosocial/cooperative phenotype. Indeed, Wilhelm, Kaltwasser, and Hilderbrandt (2017) raise a number of conceptual and statistical concerns with the Böckler et al. (2016) analyses and identified a single factor underlying prosociality in their reanalysis (however, see Böckler, Tusche, & Singer, 2018, for a reply).

However, all of this evidence is based on tasks that are costly. The costless versus costly dichotomy demonstrated here has not been modeled with respect to the prosocial phenotype, and our results suggest that there may be at least two distinct prosocial phenotypes—costly and costless. This requires further study with a wider array of preferences and real-world prosociality.

To support this further, we see that costly prosociality is associated with personality traits reflecting politeness and compassion, the two aspects of Big Five agreeableness. Previous research examining the DG shows that when this is decontextualized—as used here—politeness rather than compassion is the main predictor (Zhao et al., 2017a). However, compassion becomes a predictor of DG allocations when these are contextualized in terms of norms of need and equity (Zhao et al., 2017b). It is not surprising, therefore, that the costly prosociality component that contains both decontextualized and contextualized prosociality is associated with both compassion and politeness. Thus, costly prosociality may be motivated both by adherence to social norms and by emotional concern for others.

Interestingly, costless prosociality is distinguished by its association with intellect. With respect to organ donor registration, for example, there is some evidence that this is linked to knowledge, education, and thoughtfulness (Sperling & Gurman, 2012; Saleem et al., 2009). As such, this may, in part, account for the association of the intellect aspect of openness with costless prosociality. Thus, while it might still be normative to help, helping here may be more considered and thought through. This suggests that costless helping may be more dependent than costly helping on processes connected with cognitive engagement, such reasoning and reflection, than compassion and empathy. Indeed, this may also reflect a utilitarian principle in which utility is maximized for all with as little wastage as possible.

In contrast to our predictions, costly prosociality was not associated with increased effort (as indexed by the industriousness aspect of conscientiousness), but rather costless prosociality was linked to reduced industriousness. While this is an unpredicted finding and warrants further attention, in this context, it appears that it is the absence of expenditure of energy, effort, and resources which underlie costless prosociality. Finally, we show that the assertiveness aspect of extraversion was positively associated with costly helping. It has previously been shown that other measures of assertiveness positively predict costly punishment (negative reciprocity) with respect to rejection of unfair offers in the ultimatum game (Kaltwasser, Hilderbrandt, Whilhelm, & Sommer, 2016; Yamagishi et al., 2012), which has been interpreted in terms of status protection. However, in the context of costly prosociality without punishment, as studied here, this may specifically reflect the social, leadership, and agentic elements of volunteer behavior.

Applications

Lab-field correspondence

There is a growing literature on the capacity of lab-based prosocial preferences to predict real-world behaviors (Ostrom, 2006). In this article, economic preferences corresponded well with instances of real-world prosociality with a theoretically meaningful distinction based on costliness. One implication of our results is to identify correspondence between prosocial preferences in economic games and real-world prosociality. Here, we focused on costly versus costless prosociality. Ferguson, Taylor, Keatley, Flynn, and Lawrence (2012) focused on warm-glow preference with respect to blood donation (see also Ferguson, 2015), and Fehr and Leibbrandt (2011) on social cooperation, as indexed by PGG allocations, and variation in use of fishing techniques that were more or less likely to preserve stocks. In all these cases, the correspondence was good. When the correspondence is less clear, the associations are generally lower (Voors, Turley, Kontoleon, Bulte, & List, 2012). Thus, a clear matching of the motivations of real-world prosociality and lab-based preferences is needed.

Interventions for organ donation

The present findings offer potential implications for encouraging organ donation. Internationally, there is a major shortage of donor organs to meet the demand for transplantation. By way of example, in August 2016, there were 120,000 people in the United States and over 7,000 people in the United Kingdom on the waiting list for a solid organ transplant. Advances in transplant surgery and immunosuppression mean that outcomes following a solid organ transplant are excellent. We know from the GG results that the organ donors actually are hypergenerous, endowing their partners with more wealth than themselves. Thus, organ donors who start from a position of relative advantage are motivated, not just to redress that initial inequality but to overcompensate. This initial inequality may trigger an “advantageous inequality aversion” (Fehr & Schmidt, 1999), whereby they are motivated by guilt to reduce it, resulting in a “disadvantageous inequality aversion,” whereby the partner is now better off (O’Carroll, Foster, McGeechan, Sandford, & Ferguson, 2011). This pattern reflects exactly what is observed in an organ donation context. Initially, the donor is healthy and the recipient unhealthy (advantageous inequality aversion from the perspective of the donor), and after donation, the recipient is healthy and the donor deceased (disadvantageous inequality aversion from the perspective of the donor). Applied to the donor domain, this equates to motivating the healthy organ donors to help another whose health is poor. Thus, the following appeal, “being fit and healthy to give organs after your death means you have the ability to help those less healthy than you have a better life,” is worthy of rigorous evaluation.

Conclusion

Costliness is a major determinant of prosocial behavior, yet previous research has largely focused on costly prosociality both in the lab (e.g., giving to an anonymous partner in DGs) and in real-world prosocial behaviors (e.g., charitable giving and volunteering). In the current study, we identified distinct components of costly and costless prosociality (across self-reports and economic games) that were driven by different personality traits and are likely to reflect different motivations. This distinction highlights the multifaceted nature of prosociality and has important implications for how different types of prosocial behaviors can be promoted in the real world.

Supplemental Material

Supplemental Material, SPPS765071_suppl_mat - Costless and Costly Prosociality: Correspondence Among Personality Traits, Economic Preferences, and Real-World Prosociality

Supplemental Material, SPPS765071_suppl_mat for Costless and Costly Prosociality: Correspondence Among Personality Traits, Economic Preferences, and Real-World Prosociality by Eamonn Ferguson, Kun Zhao, Ronan E. O'Carroll and Luke D. Smillie in Social Psychological and Personality Science

Behaviors	Factors
Donate your unwanted clothes to charity	.913	.000	.000	.000
Give someone a concert ticket for free that you can no longer use	.554	.000	.000	.000
Donate any unwanted toys to charity	.690	.000	.000	.000
Donating 20% of your salary to charity	.000	.617	.000	.000
Donate money to help those you think have unfairly lost their jobs	.000	.780	.000	.000
Volunteer your time regularly to help out at a local charity shop	.000	.377	.000	.000
Donate blood once	.000	.000	.806	.000
Donate blood regularly	.000	.000	.831	.000
Give someone your car parking ticket, for free, when there is still time left on it	.000	.000	.000	.604
Register as an organ donor to donate organs after your death	.000	.000	.000	.553
Sign a petition	.000	.000	.000	.658
Vote in a general election	.000	.000	.000	.672
Correlations across latent factors
Factor 1: Costless nonhealth prosociality	1
Factor 2: Costly nonhealth prosociality	.381	1
Factor 3: Costly health prosociality: blood donation	.278	.116	1
Factor 4: Costless communal and civic duty	.521	−.063	.415	1

Models	χ² (df)	CFI	TLI	RMSEA
Model 1: One factor	311.06 (54)***	.59	.49	.15***
Model 2: Two factors (costly vs. costless)	255.85 (53)***	.72	.65	.13***
Model 3: Two factors (health vs. nonhealth)	226.70 (53)***	.72	.65	.13***
Model 4: Four factors (health, nonhealth, costly, and costless)—all orthogonal	106.14 (42)***	.90	.84	.09***
Model 5: Four-factor (health vs. nonhealth, costly vs. costless)—health versus nonhealth, costly versus costless—oblique	66.41 (40)***	.96	.93	.06
Modified model
Model 6: Four-factor oblique—costless (generic or communal/civic) and costly (health or nonhealth)	74.97 (48)**	.96	.94	.05

Trait (Aspect)	Description	Example Item	α
Neuroticism
Withdrawal	Tendency to experience anxious and depressive traits	Seldom feel blue	.88
Volatility	Tendency to be irritated, angry, and emotionally labile	Get angry easily	.90
Agreeableness
Politeness	Tendency to respect others and adhere to social norms	Hate to seem pushy	.78
Compassion	Tendency to be emotionally concerned about others’ well-being	Sympathize with others’ feelings	.90
Conscientiousness
Industriousness	Tendency to be focused on goal pursuit and carry out plans	Finish what I start	.87
Orderliness	Tendency to be rule-abiding and organized	Follow a schedule	.81
Extraversion
Enthusiasm	Tendency to be gregarious and to experience positive emotions	Warm up quickly to others	.88
Assertiveness	Tendency to be bold and dominant	Take charge	.91
Openness/intellect
Openness	Tendency for engagement with perceptual and aesthetic domains	Enjoy the beauty of nature	.82
Intellect	Tendency for intellectual curiosity and engagement	Like to solve complex problems	.88

	Sample 1	Sample 2	Sample 3	Sample 4
Number (%) removed after comprehension and/or attention checks	33 (32%); four comprehension checks of games and one of two attention checks embedded within personality measures	26 (7.9%); two attention checks embedded within personality measures	19 (15.6%); two attention checks embedded within personality measures	10 (3.8%); two attention checks embedded within personality measures
Participants	University students and community members in Australia	U.S. Amazon MTurk workers (with fewer than 50 Human Intelligence Tasks)	First-year psychology students at an Australian university	U.S. Amazon MTurk workers
Age	18–33 years (M _age = 22.34, SD = 3.76)	18–65 years (M _age = 30.90, SD = 9.89)	18–47 years (M _age = 19.63, SD = 3.71)	19–67 years (M _age = 34.76, SD = 11.00)
Female (%)	65.7	54.9	75.7	42.6
Organ donor (%)	25.7	66.8	29.1	53.5
Administration of study	Online Qualtrics survey	Online Qualtrics survey	Online Qualtrics survey	Online Qualtrics survey
Median study time	64 min	30 min	43 min	42 min
Show-up fee	AUD$ 15.00	US$2.00	Course credit	US$8.00
Game format	Full description of dictator and generosity game	Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions	Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions	Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions
Incentivization	Incentivized: participants played a number of economic games in which they were matched with another player, and one of these games was selected for payment at the rate of 1 point = AUD$1.00	Hypothetical: participants asked to imagine that they were playing with a stranger that they would not knowingly meet	Hypothetical: participants asked to imagine that they were playing with a stranger that they would not knowingly meet	Incentivized: participants played a number of economic games in which they were matched with another player, and one of these games was selected for payment at the rate of 1 point = US$0.10
Economic games in study	Dictator game, ultimatum game (proposer), ultimatum game (responder), and generosity game. Participants were randomly assigned to one of four different orders of these games based on a Latin squares design.The order of the games was not associated with responses in the dictator and generosity games.	Real-world dictator game, dictator game, and generosity game (the latter two were randomized).Experimental condition for the real-world dictator game was not associated with responses in the later dictator and generosity games.	Giving/taking framed dictator game, social mindfulness task, dictator game, and generosity game (the latter two were randomized).Giving/taking framing of the earlier dictator game was not associated with responses in the later dictator and generosity games.	Giving/taking framed dictator game, dictator game, followed by generosity game.Giving/taking framing of the earlier dictator game was not associated with responses in the later dictator and generosity games.

Predictor	ρ	OLS	95% CI	Tobit	95% CI
Organ donation (0 = no, 1 = yes)	.15***	0.85***	[0.44, 1.26]	1.45***	[0.76, 2.12]
Charity/volunteer	.03	0.05	[−0.04, 0.15]	0.09	[−0.07, 0.25]
Sex (1 = male, 2 = female)	−.19***	−0.93***	[−1.33, −0.52]	−1.58***	[−2.26, −0.91]
Incentives (1 = incentivized, 2 = hypothetical)	−.16***	−1.69***	[−2.55, −0.84]	−2.79***	[−4.23, −1.34]
Sample (0 = student, 1 = MTurk)	.06	−2.00*	[−3.56, −0.44]	−1.42*	[−2.69, −0.125]
Dictator game	−.11**	−0.02	[−0.13, 0.09]	0.09	[−0.28, 0.08]
Generosity game
Incentives × Sample		1.21*	[0.27, 2.14]	1.97*	[0.39, 3.55]
R ²		.08***		.02	[0.76, 2.12]
N	729–732	728		728

Predictor	ρ	OLS	95% CI	Tobit	95% CI
Organ donation (0 = no, 1 = yes)	.05	−0.06	[−0.34, 0.22]	−0.06	[−0.40, 0.27]
Charity/volunteer	.13**	0.10**	[0.03, 0.16]	0.11**	[0.03, 0.19]
Sex (1 = male, 2 = female)	.14**	0.33*	[0.06, 0.61]	0.41*	[0.07, 0.74]
Incentives (1 = incentivized, 2 = hypothetical)	.40***	2.32***	[1.76, 2.88]	2.69***	[2.01, 3.38]
Sample (0 = student, 1 = MTurk)	.05	1.94**	[0.88, 2.99]	1.04**	[0.43, 1.65]
Dictator game
Generosity game	−.11**	−0.01	[−0.06, 0.04]	−0.01	[−0.07, 0.04]
Incentives × Sample		−1.02**	[−1.65, −0.38]	−1.13**	[−1.91, −0.36]
R ²		.19***		.05
N	729–732	728		728

Prosociality Measures	Costly Helping	Costless Helping
Charity	.99	.00
Volunteering	.39	.00
Dictator game	.16	.00
Generosity game	.00	.76
Organ donation	.00	.26
	Latent correlations
	1
.26	1

Predictor	Costly	Costless
N: Withdrawal	−.10	−0.37	[−0.83, 0.09]	−.06	−0.22	[−0.67, 0.24]
N: Volatility	.02	0.09	[−0.31, 0.49]	−.01	−0.05	[−0.45, 0.35]
A: Compassion	.18**	0.85**	[0.37, 1.33]	−.01	−0.06	[−0.53, 0.42]
A: Politeness	.13***	0.68***	[0.17, 1.19]	.14**	0.74**	[0.24, 1.24]
C: Industriousness	−.05	−0.23	[−0.74, 0.28]	−.15*	−0.61*	[−1.12, −0.11]
C: Orderliness	.03	0.16	[−0.25, 0.57]	−.04	−0.17	[−0.57, 0.23]
E: Enthusiasm	−.08	−0.34	[−0.76, 0.08]	.01	0.05	[−0.36, 0.46]
E: Assertiveness	.15**	0.58**	[0.14, 1.02]	−.08	−0.30	[−0.73, 0.13]
O: Intellect	−.01	−0.01	[−0.45, 0.44]	.25***	1.07***	[0.63, 1.51]
O: Openness	.01	0.04	[−0.36, 0.45]	−.08	−0.39	[−0.78, 0.01]
Gender (1 = male, 2= female)	.13***	0.76***	[0.31, 1.21]	−.14***	−0.83***	[−1.28, −0.38]
Incentives ( 1 = incentivized, 2 = hypothetical)	.24***	1.45***	[0.58, 2.31]	−.32***	−1.87***	[−2.72, −1.03]
Sample (0 = student, 1 = MTurk)	.05	0.38	[−1.25, 2.01]	−.30*	−2.00*	[−3.61, −0.40]
Incentives × Sample	−.04	−0.14	[−1.13, 0.85]	.40**	1.47**	[0.50, 2.44]
N		728			729
R ²		.17			.12

Footnotes

Authors’ Note

The data can be obtained from either the first author on request or from the Open Science Framework (osf.io/cnwyk/).

Acknowledgments

Basu Madhurima for help with data collection for Study 1 and Freya Harrison for help with data collection for Supplementary data Text S1.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Preparation of this manuscript was supported by funding from the Melbourne School of Psychological Sciences, the University of Melbourne and the School of Psychology, University of Nottingham. Kun Zhao was supported by an Australian Postgraduate Award and an Endeavour Research Fellowship.

ORCID iD

Eamonn Ferguson

Supplemental Material

The supplemental material is available in the online version of the article.

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	Sample 1	Sample 2	Sample 3	Sample 4
Descriptors	N = 70	N = 304	N = 103	N = 256
Number (%) removed after comprehension and/or attention checks	33 (32%); four comprehension checks of games and one of two attention checks embedded within personality measures	26 (7.9%); two attention checks embedded within personality measures	19 (15.6%); two attention checks embedded within personality measures	10 (3.8%); two attention checks embedded within personality measures
Participants	University students and community members in Australia	U.S. Amazon MTurk workers (with fewer than 50 Human Intelligence Tasks)	First-year psychology students at an Australian university	U.S. Amazon MTurk workers
Age	18–33 years (M _age = 22.34, SD = 3.76)	18–65 years (M _age = 30.90, SD = 9.89)	18–47 years (M _age = 19.63, SD = 3.71)	19–67 years (M _age = 34.76, SD = 11.00)
Female (%)	65.7	54.9	75.7	42.6
Organ donor (%)	25.7	66.8	29.1	53.5
Administration of study	Online Qualtrics survey	Online Qualtrics survey	Online Qualtrics survey	Online Qualtrics survey
Median study time	64 min	30 min	43 min	42 min
Show-up fee	AUD$ 15.00	US$2.00	Course credit	US$8.00
Game format	Full description of dictator and generosity game	Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions	Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions	Decomposed: dictator and generosity games embedded within a series of different decomposed economic allocation decisions
Incentivization	Incentivized: participants played a number of economic games in which they were matched with another player, and one of these games was selected for payment at the rate of 1 point = AUD$1.00	Hypothetical: participants asked to imagine that they were playing with a stranger that they would not knowingly meet	Hypothetical: participants asked to imagine that they were playing with a stranger that they would not knowingly meet	Incentivized: participants played a number of economic games in which they were matched with another player, and one of these games was selected for payment at the rate of 1 point = US$0.10
Economic games in study	Dictator game, ultimatum game (proposer), ultimatum game (responder), and generosity game. Participants were randomly assigned to one of four different orders of these games based on a Latin squares design.The order of the games was not associated with responses in the dictator and generosity games.	Real-world dictator game, dictator game, and generosity game (the latter two were randomized).Experimental condition for the real-world dictator game was not associated with responses in the later dictator and generosity games.	Giving/taking framed dictator game, social mindfulness task, dictator game, and generosity game (the latter two were randomized).Giving/taking framing of the earlier dictator game was not associated with responses in the later dictator and generosity games.	Giving/taking framed dictator game, dictator game, followed by generosity game.Giving/taking framing of the earlier dictator game was not associated with responses in the later dictator and generosity games.