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The Role of Trust in the Social Heuristic Hypothesis Andrés Montealegre

Universidad de los Andes

Author Note

Supervisors: Dr. William Jiménez and Dr. Juan Camilo Cárdenas

Awards: Miguel Salas Award for Best Undergraduate Psychology Thesis (November, 2015) Presentations: Annual Conference of the Society for Judgment and Decision Making (November, 2015) and Congreso Colombiano de Psicología y 1ra Conferencia Regional

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Table of Contents

Abstract………...3

Acknowledgments………4

Literature Review……….…5

Method and Results………...…….12

Discussion………...27

References………...32

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Abstract

Are we intuitively cooperative or selfish? According to the social heuristic hypothesis (SHH), people develop cooperative intuitions in their daily life’s that guide their behavior in unrelated domains. Specifically, the SHH argues that the relation between intuition and cooperation is moderated by trust in daily life interactions and experience with economic games. While various studies provide support for the SHH there are several open questions. In the present project we study the impact of a behavioral manipulation of trust and explore alternative measures of trust. In addition, we examine the role of a moderating variable: information processing preferences. Results show that the behavioral manipulation of trust increases cooperation. This effect appears to be moderated by information processing preferences rather than cognitive process manipulations. Furthermore, an experimental measure of trust is a better predictor of cooperation than self-report. These findings have implications for the psychology of cooperation. Finally, we suggest potential interventions to increase cooperation in the real world.

Keywords: behavioral decision-making, prosocial behavior, trust, cooperation,

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Acknowledgments

Special thanks to William Jiménez and Juan Camilo Cárdenas whose wonderful supervision and support made this project possible. Thanks to the Psychology Department and the Faculty of Economics from Universidad de los Andes for funding the lab experiments. The paper was improved through thoughtful comments made by Andrés Moya, Christopher Olivola and members of the Cognition Lab. Thanks to the great team of research assistants who made data collection possible: Christian Ortega, Felipe González, Samir Cure, Carlos Cortés, Andrea González, Angie Corredor, Daniela Duque and Astrid Niyireth. Thanks to Javier Corredor for allowing us to collect data in his lab at the Universidad Nacional. Thanks to David Rand, Anthony Evans and Juan Camilo Cárdenas for sharing materials for the experiments. We used the materials and protocol from the Rand et al. (2012) Open Science Framework project for the present experiments.

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Literature Review

“Trust is an important lubricant of a social system. It is extremely efficient; it saves a lot of trouble to have a fair degree of reliance on other people's word. Unfortunately, this is not a commodity which can be bought very easily. If you have to buy it, you already have some doubts about what you have bought.” (Arrow, 1974, p. 23)

Widespread cooperation is an important characteristic of human societies (Melis & Semmann, 2010). Cooperation has been defined as a behavior “in which an individual pays a personal cost to provide a benefit to another individual or group of individuals” (Jordan, Peysakhovich, & Rand, 2014, p. 87). Social or cooperative dilemmas can be defined as

situations where “there is tension between what is good for the individual and what is good for the population. The population does best if individuals cooperate, but for each individual there is a temptation to defect” (Rand & Nowak, 2013, p. 413). Social dilemmas encompass a broad range of situations, from small-scale phenomena such as our personal relationships up to big-scale phenomena such as global warming and traffic. Needless to say, many of the world’s most pressing problems constitute cooperative dilemmas.

Much work has been devoted to the study of the evolution of cooperation. From an evolutionary perspective based on natural selection cooperation is challenging to explain. However, there are some mechanisms -apart from natural selection- than can account for the evolution of cooperation (Rand & Nowak, 2013). Another approach considers the cognitive basis of cooperation (Rand, Greene, & Nowak, 2012; Rand et al., 2014a). The dual process framework, which argues that judgments result from both intuitive (system 1) and deliberative (system 2) processes (Greene, 2013; Kahneman, 2011; Sloman, 1996), has been applied to cooperation. The fundamental question that has guided this line of research is: are we

intuitively cooperative or selfish? Is our first instinct to cooperate or to act in our own interest? (Rand et al., 2014a).

Intuition and Cooperation

Different research strands support the idea that we are intuitively cooperative. Evidence from neuroscience shows that prosocial behaviors are associated with value and reward-seeking circuits (mesolimbic dopaminergic system) and not effortful control regions (lateral prefrontal cortex). Developmental research shows that prosocial behaviors appear early in life, before the capacity to exercise effortful control has developed (Zaki & Mitchell,

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2013). Of particular interest, behavioral studies show that people who take faster (and presumably more intuitive) decisions tend to be more cooperative, while people who make slower (and presumably more reflective) decisions tend to be more selfish (Rand et al., 2012, studies 1-5 and 10). However, the sole use of decision times to infer whether we are intuitively cooperative or selfish is misleading (Krajbich, Bartling, Hare, & Fehr, 2015). In fact, Evans, Dillon and Rand (2015) show that reaction times are driven by decision conflict –rather than intuition or reflection- and, therefore, “should not be interpreted as a direct proxy for the use of intuitive or reflective processes” (p. 951). Nonetheless, by directly manipulating cognitive processes one can avoid this pitfall. Accordingly, studies that induce intuitive processes show that making subjects respond under time pressure (Rand et al., 2012, studies 6-7; Cone & Rand, 2014; Rand et al., 2014a; Rand, Newman, & Wurzbacher, 2014b; Rand & Kraft-Todd, 2014c; Evans et at., 2015), priming intuitive processes (Rand et al., 2012, studies 8-9; Lotz, 2014; Evans et al., 2015) and lowering impulse control (De Dreu, Dussel, & Ten Welden, 2015) increases cooperation. In addition, there is evidence of intuitive prosociality in other social preference domains such as altruism (Cornelissen, Dewitte, & Warlop, 2011; Schulz, Fischbacher, Thöni, & Utikal, 2012) and fairness (Cappelletti, Güth, & Ploner, 2011). The cumulative behavioral evidence showing a relationship between intuition and cooperation has given rise to a labeled intuitive-cooperation effect.

However, some studies that have failed to replicate this finding. Tinghög et al. (2013, study 5) conduct a study in which they fail to replicate results from Rand et al. (2012). In addition, they highlight two problems from their studies 6 and 7: first, excluding participants who fail to respond on time generates a selection problem by increasing participants who cooperate and, second, incorrectly controlling for ‘answering on time’ in the supplementary analyses facilitates the appearance of an intuitive-cooperation effect (without controlling for this variable the effect disappears). Nonetheless, Rand, Greene and Nowak (2013) reply by demonstrating that the effect of time pressure on cooperation is maintained when including subjects that fail to respond on time and showing that over an aggregate analysis of 15 studies intuitive decisions tend to be, on average, more cooperative. Further on, the authors argue that some of the negative results found by Tinghög et al. (2013) can be explained by

methodological modifications that probably suppress the time pressure effect: first, making other economic decisions before playing cooperation games and, second, applying time

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pressure to subjects when obtaining information about the payment structure of the game. Therefore, although there are both positive and null results in the literature the overall pattern appears to be that time pressure increases cooperation.

However, the relationship between intuition and cooperation is more complex. To explain variation across studies Rand et al. (2014a) propose the social heuristic hypothesis (SHH). According to the SHH people develop cooperative intuitions in their daily life’s that influence their behavior in unrelated domains. Specifically, it suggests that the relationship between intuition and cooperation is moderated by trust in daily life interactions and experience with economic games. If people find it advantageous to cooperate in their daily lives -and therefore have high levels of interpersonal trust- intuitive cooperation should be higher. However, for subjects who find that cooperation is not advantageous in their daily lives -and therefore have low levels of interpersonal trust- the SHH predicts that they will always defect. In addition, experience with one shot-anonymous economic games gives people an ability to update their intuitions. Specifically, people with more experience tend to adjust their default response towards the more advantageous behavior in the current situation, which in the present games (one-shot anonymous interactions) is to defect. Therefore, intuitive cooperation should be higher among people who find cooperation advantageous in their daily lives and have no experience with economic games (Rand et al., 2014a; Rand & Kraft-Todd, 2014c).

Several studies provide support for the SHH. A cumulative analysis of 15 studies and 6,910 decisions shows that time pressure, on average, increases cooperation. Nonetheless, the intuitive-cooperation effect has declined and disappeared over a two-year period among MTurk users. This result can be partially explained by the fact that users have become experienced to one shot-shot economic games, with an increase in experience allowing users to update their intuitions and reducing cooperation (Rand et al., 2014a). Furthermore, inducing intuitive processes increases cooperation among subjects who have trusting daily life

interactions (Rand & Kraft-Todd, 2014c).

However, some studies still show results that challenge the SHH. A recent experiment by Verkoeijen and Bouwmeester (2014, study 3) failed to replicate the intuitive-cooperation effect among MTurk users with no experience with economic games. In addition, some studies show a relationship between self-control and cooperation that contradicts the

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intuitive-cooperation effect (Kocher, Myrseth, Martinsson, & Wollbrant, 2012; Martinsson, Myrseth, & Wollbrant, 2014), insofar as self-control is regarded as a system 2 process. However, the ongoing project from Open Science Framework to replicate study 7 from Rand et al. (2012) will probably shed light on the exact nature of the intuitive-cooperation effect. Therefore, although most of the evidence supports the SHH (e.g. Rand et al. 2012; Rand et al., 2014a), unsuccessful replications (Tinghög et al., 2013, study 5; Verkoeijen & Bouwmeester, 2014, study 3) and contradictory evidence (Kocher et al., 2012; Martinsson et al., 2014) still cast doubt on the exact relation between intuition and cooperation.

Extensions of the Social Heuristic Hypothesis

Numerous studies have extended the SHH. Empirical studies have taken several directions, including experimental manipulation of norms, social context, contextual framing and extension to a non-cooperative location. For instance, Peysakhovich and Rand (2013) show that norms can be manipulated experimentally and influence the behavior of subjects in one-shot economic games. Specifically, exposing participants to a repeated prisoner’s

dilemma that incentivizes cooperation increases their prosociality, altruistic punishment and trust, while exposing participants to a repeated prisoner’s dilemma that disincentives

cooperation has the opposite effect. The result is stronger among subjects that rely on heuristic processing (as measured by the CRT). Moreover, the authors provide tentative evidence that the effect is driven by a modification of preferences rather than a change of beliefs concerning the contribution of other players.

In another study, Rand, Newman and Wurzbacher (2014b) examine whether

manipulating the social context alters the relationship between time pressure and cooperation. Study 1 manipulates group membership and study 2 manipulates framing the gaming as collaboration vs. competition. The results show that the manipulations do not alter the

relationship between time pressure and cooperation. Social context provides an initial anchor that can be overridden by reflective processes. This studies show that prior findings are not the result of conducting experiments in decontextualized settings.

Likewise, Cone and Rand (2014) examine whether a contextual framing (competitive vs. cooperative) moderates the relationship between time pressure/delay on cooperation. The results show an interaction between time pressure and contextual framing. In the competitive

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framing, but not in the cooperative framing, participants cooperate more under time pressure than under time delay. The fact that participants do not cooperate more under time pressure in the cooperative framing is explained by appealing to participant’s levels of experience. The positive results in the competitive framing condition are taken as evidence that changing the standard framing of the game (cooperative framing) can make it appear novel to participants.

In addition, Rand and Kraft-Todd (2014c) show that there is a positive three-way interaction between trust, naivety and time pressure in social dilemmas, and that time pressure doesn’t increase cooperation when there is no social dilemma. Further on, the authors provide evidence that experience reduces cooperation by teaching subjects not to rely on their

intuitions.

Moreover, Capraro, Jordan and Rand (2014) examine the motivation of participants in a one-shot prisoner’s dilemma. To analyze the distribution of cooperation, decisions were presented as continuous (0-1) rather than dichotomous (defect-cooperate). The benefit to cost ratio (b/c) was manipulated to study its impact on cooperation levels. Additionally, the

experiment examines whether the behavior of participants in a dictator game is correlated with their behavior in a prisoner’s dilemma. The results showed a tri-modal distribution, in which most of the participants gave all, gave half or gave nothing. Also, increasing the b/c ratio moved many participants that gave nothing to give everything. The results are interpreted as evidence that although strategic concerns are at play there is a substantial portion of

participants who cooperate regardless of the b/c ratio, which is probably the result of heuristic rather than controlled processing. In addition, a correlation was found between the behavior of participants in the dictator game and the prisoner’s dilemma game.

Finally, Capraro and Cococcioni (2015) conduct an experiment in a non-cooperative setting (India) and show that time pressure doesn’t increase cooperation among naïve participants with non-cooperative daily life interactions, providing evidence for the negative effect of non-cooperative daily life interactions on cooperation. Additionally, they find that inducing intuition increases cooperation more among experienced than among naïve participants. Although it is not clear how to explain the positive effect of experience on cooperation (in previous studies conducted in the US experience had a negative effect), some tentative explanations are proposed: users are misapplying a strategy learned in iterated games or users are generating a sense of community in MTurk.

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In short, studies have extended the SHH by showing that norms can be manipulated experimentally; the relation between intuition and cooperation is not affected by the social context; changing the framing of the game can reestablish the relation between intuition and cooperation for experienced participants; strategic concerns cannot entirely explain why participants cooperate in one-shot anonymous games, and the findings can be extended to participants with non-cooperative daily-life interactions.

Social Heuristic Hypothesis and Trust

In the present project we seek to examine the link between cooperation and trust, in light of the SHH. Trust in daily life interactions is a key moderating variable in the SHH, although it has not been studied in depth. Regarding trust, the SHH predicts that inducing intuitive responses among subjects who find cooperation advantageous in their daily lives (and therefore have high levels of interpersonal trust) should increase cooperation. However, for subjects whom cooperation is not advantageous in their daily lives (and therefore have low levels of interpersonal trust) inducing intuitive responses should have no effect (Rand et al., 2014a; Rand-Kraft-Todd, 2014c). These predictions receive preliminary support by Rand et al. (2012) who finds that participant’s who respond faster cooperate more only if they have trusting daily interactions. However, a limitation is the correlational nature of this result. However, Rand and Kraft-Todd (2014c) show that there is a positive three-way interaction between trust, naivety and time pressure, demonstrating that inducing intuitive processes increases cooperation among participants that are both trusting and naïve. Furthermore, Peysakhovich and Rand (2013) show that exposing participants to environments that support or undermine cooperation influences their subsequent cooperative behavior. Finally, Capraro and Cococcioni (2015) show that inducing intuitive processes doesn’t increase cooperation among naïve participants with non-cooperative daily life interactions. However, they find that time pressure increases cooperation among more experienced participants. In summary, some correlational and experimental evidence supports the relevance of trust in the SHH.

Still, there are several open-questions that deserve further attention (Rand & Kraft-Todd, 2014c). First, it is necessary to study the causal impact of trust on intuitive cooperation, rather than just measuring it ex post facto. Second, it is necessary to study trust through an experimental approach rather than self-report, given its limitations (Baumeister, Vohs, &

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Funder, 2007). In the present paper we attempt to address these questions.

Issues in the Study of Trust

There are conceptual and practical issues in the study of trust. On the one hand, the study of trust is complicated by the lack of agreement regarding an appropriate definition (Fehr, 2009). While some definitions underscore expectations and predictability (Balliet & Van Lange, 2012), others emphasize expectations in situations where individual interests are at odds with collective interests (Balliet & Van Lange, 2012) and others highlight the role of beliefs and preferences (Coleman, 1990). According to a classic definition that fits in the second category "trust is a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another" (Rousseau, Sitkin, Burt, & Camerer, 1998, p. 395).

On the other hand, the picture is complicated by different approaches in economics and psychology to understand trust (Evans & Krueger, 2009). To study trust, economists use economic games and explain it through social preferences and norms. In contrast,

psychologists explain it through individual differences, social identity and expectations. A multidimensional perspective appears to be necessary to understand and measure the complex nature of trust (Evans & Krueger, 2009).

A further complication is that it is not clear whether survey measures, experimental behavior and real world behavior are correlated. On the one hand, Cárdenas, Chong and Ñopo (2013) find that an experimental measure of trust (Trust Game: TG) predicts real world prosocial behavior while stated preferences regarding prosociality don’t. It must be noted that some of the previous measures are not directly aimed at measuring trust and, therefore, are probably second best. On the other hand, Evans and Revelle (2008) present a self-report instrument (Propensity to Trust Survey: PTS) to measure trust that predicts experimental behavior (TG). Thus, while the issue far from solved, the TG and the PTS appear to be adequate experimental and survey measures of trust, respectively. Hence, difficulties in investigating trust are due to a lack of consensus regarding an appropriate definition, dissimilar approaches in psychology and economics, and uncertainty regarding external validity.

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Method and Results

In the present project we examined the role of trust in SHH. We conducted two lab-experiments that involve one-shot public good games (PGG) under time pressure and time delay. We conducted lab instead of online experiments because MTurk participants have become too experienced with economic games and the intuitive-cooperation effect has disappeared among this users (Rand et al., 2014a). The procedure in our studies is similar to study 7 by Rand et al. (2012) and borrows from the materials and protocol of the replication report for the same study in Open Science Framework1. In experiment 1, we studied the impact of a behavioral manipulation of trust on intuitive cooperation. In particular, we

expected priming a high trust situation to increase cooperation under time pressure but have no effect under time delay. This hypothesis is grounded in the SHH’s proposal of the role of intuition and reflection (Rand et al., 2014a). According to the SHH, cooperative norms developed under trusting daily life interactions spill to unrelated domains when making decisions under time pressure but not under time delay. In addition, we examined an individual difference moderator: preference for information processing.

In experiment 2, we explored alternative measures of trust: The Propensity to Trust Survey (PTS: Evans & Revelle, 2008) and the Trust Game (TG: Berg, Dickhaut, & McCabe, 1995). We expected subjects who scored higher on any of the trust measures to contribute more under time pressure than under time delay. This hypothesis is supported in the SHH, which posits that intuitive cooperation should be higher among subjects who have trusting daily life interactions (Rand et al., 2014a; Rand- Kraft-Todd, 2014c). There is no particular hypothesis regarding which of the measures is expected to be a better predictor of intuitive cooperation.

Experiment 1 Participants and Design

295 Participants (167 men, 125 women and 2 other, average age=21.92, SD= 3.26) participated in the experiment. We determined the sample size prior to the study by conducting a power analysis (see appendix 1A). Participants were undergraduate students                                                                                                                

1 Open Science Framework RRR - Rand et al., (2012) - intuitive cooperation: https://osf.io/scu2f/

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under the age of 35. Regarding experimental design, the study had a 2 (trust manipulation: high vs. low) ×  2 (cognitive process manipulation: time pressure vs. time delay) between subject design. Participants were randomly assigned to one of the four conditions: high trust ×   time pressure, high trust ×  time delay, low trust ×  time pressure, or low trust ×  time delay.  

The purpose of the trust manipulations was to prime a high or low interpersonal trust mindset. We modified a priming procedure similar to the one used in previous studies to induce intuitive processes (Rand et al., 2012; Shenhav, Rand, & Greene, 2012). In the high trust condition participants were asked to write a paragraph (8-10 sentences) describing a moment in their lives in which trusting other people led them to positive consequences. In the low trust condition participants were asked to write a paragraph (8-10 sentences) describing a moment in their life’s in which trusting other people led them to bad consequences. The low trust condition is a form of control. To make the connection between the trust manipulation and the PGG less transparent we presented the manipulation as a writing exercise to verify that the participants were concentrated. Furthermore, to examine whether our findings are the result of demand effects (see Zizzo, 2010) we measured participant’s perceived awareness of the research hypothesis (PARH: Rubin, Paolini, & Crisp, 2010). Additionally, to verify that we actually increased trust we included a manipulation check (e.g. how much trust did you experienced at the moment of the decision in the game?). The question was included among other similar questions related to other emotions to conceal the purpose. Moreover, to obtain an independent measure of self- reported trust we asked participants to answer a survey before the experiment. To avoid influencing participants’ behavior, we required the survey to be answered at least one week before the experiment. We introduced 13 filler items from the World Value Survey to hide the purpose of the survey.

Cooperation was measured as the amount that participants contributed to the common pool in the PGG. The PGG was conducted in groups of four players. In the game, each player had an endowment of 8,000 COP2 and has to decide how much to contribute to a common pool (Camerer & Fehr, 2002). The contributions made by players are multiplied by 2 and are                                                                                                                

2  In the original experiment (Rand et al., 2012, study 7) the endowment for the public goods game was 4 dollars. To adjust this amount for differences in currency and utility we: 1) converted from USD to COP (4 US dollars = 12,103.05 COP) and 2) adjusted by local purchasing power: 12,103.05 COP × (1 / 0.6934) = 8,392.255 COP and approximated to 8,000.000 COP.

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then divided evenly among players, regardless of their contribution. The game has a marginal return of 0.5. The PGG has the basic structure of a social dilemma insofar as “players have incentives to contribute nothing to the public good, but contributions from everyone would make everyone better off” (Camerer & Fehr, 2002, p. 9). The game was played in real time: players made their decisions and were immediately matched randomly with other players. The game involved real incentives and no deception. However, only 1 out of every 4 players was paid the amount won in the game. The winners were determined through a random lottery. After the game, we measured participant’s beliefs regarding the contribution of other players, comprehension of the task and justification for their contribution.

The purpose of the cognitive process manipulations was to induce intuitive and deliberative processes with time pressure and time delay, respectively. These manipulations were introduced at the moment of the decision in the PGG. In the time pressure condition subjects were asked to answer as fast as possible, with a maximum time of 10 seconds. In the time delay conditions subjects were told to carefully consider their decision, and were asked to wait a minimum of 10 seconds to answer.

We also measured participant’s preference for information processing as measured by the short version of the Rational-Experiential Inventory (REI-10: Norris, Pacini, & Epstein, 1998). The REI-10 is composed of 10 questions divided in two subscales: Need for Cognition (5 items) and Faith in Intuition (5 items). We choose the REI instead of the Cognitive

Reflection Test (CRT: Frederick, 2005) to measure the role of preference for an intuitive processing style, due to recent findings showing that the CRT is not a valid measure of intuition (Pennycook, Cheyne, Koehler, & Fugelsang, 2015). The order of the questions was randomized. We also measured experience with economic games and experience with research participation more generally. To examine the relation between prosocial behavior in the lab and in the real world we measured participant’s social capital (Cardenas, Chong & Ñopo, 2013). Finally, we asked a series of demographic questions (sex, age, country, undergraduate program, social strata and subjective socioeconomic status) and measured how many of the participants in the room they know.

Procedure

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to a decision-making study in which they could win up to $26,000 COP and a minimum of $6,000 COP (the show up fee was $6,000). The experiment lasted approximately 25 minutes. Subjects signed up by choosing a session and filling a pre-experimental survey at least one week before the chosen session. The experiments were conducted by research assistants trained in behavioral research. Before the session the experimenters (at least 2 per session) opened the Qualtrics survey in each computer. Afterwards, subjects entered the lab and waited in each computer station in silence while the rest of the participants arrived. Only sessions of 8 or 12 participants were conducted to ensure subjects were not able to identify their group members in the game. When all subjects were seated the experimenter gave instructions and instructed participants to begin. When all participants finished, the experimenter called each participant –one by one- to claim his payment in an adjacent room. Participants were informed about their result in the game and whether or not they won the random lottery. They were asked to avoid telling other people about the purpose of the study. A summary of the study and the order of the tasks can be found in figure 1.

Pre-experimental

survey Experiment Structure Payment

Trust questions

High or low trust induction (random)

PGG under TP or TD (rando

m)

Belief, justification, understanding

Trust manipulati

on check

Short REI-10 (randomiz

ed)

Experience, social capital,

PARH Demographics experiment After the

Figure 1: Summary of experiment 1

Results

The purpose of experiment 1 was to explore the impact of a behavioral manipulation of trust on intuitive cooperation. We start with some descriptive statistics. Participants

contributed a mean of $5,075 COP in the PGG (SD = 2,671.55, N = 295), with those in the high trust condition contributing a mean of $5,390.8 COP (SD = 2,715.22, N = 148) and those in the low trust condition a mean of $4756.56 COP (SD = 2,597.3, N = 147). In addition, participants under time pressure contributed a mean of $5,020.84 COP (SD = 2,617.19, N = 149) and those under time delay a mean of $5,129.77 COP (SD = 2733.82, N = 146).

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Regarding comprehension of the game, 55% answered both questions correctly, while 45% failed in least one question.

We then analyzed whether contribution rates were predicted by the interaction between the high trust induction and time pressure. An important point is that participants who failed to obey the time constraint were not excluded to avoid selection problems (Tinhog et al., 2013). A linear regression showed that the interaction does not predict contribution rates (F(1, 293) = 0.75, p > .05, R2 = 0.003), suggesting the interaction between trust and cognitive process manipulation is not related to contribution rates.

We then examined whether the cognitive process manipulation had a main effect on cooperation. However, a linear regression showed there is no significant main effect of time pressure on contribution rates, F(1, 293) =  0.12, p > .05, R2 = 0.0004. To further scrutinize this result we analyzed the decision times under the different cognitive process manipulations. Under time pressure the mean decision time was 15.44 seconds (SD = 8.31, N = 149), while under time delay the mean decision time was 33.95 seconds (SD = 30.81, N = 146). A welch two sample t-test showed that this difference is significant (t(165.58) = 7.01, p < .01),

suggesting the cognitive process manipulations had an effect on decision times. Additionally, we analyzed the distribution of the decision times and found it is right-skewed (see figure 2). Thus, we applied a log-10 transformation in order to avoid extreme responses from having an excessive influence (see figure 3; Rand et al., 2012). However, results showed that after applying this transformation the difference between time pressure and time delay is still significant (t(253.02) = 9.43, p < .01), indicating the result are similar after reducing the influence of extreme observations. Moreover, a linear regression showed that the time log-10 decision time predicts the amount of contribution (F(1, 293) = 11.22, p < .001, R2 = 0.04), suggesting that participants who took longer cooperated less. To sum up, while the cognitive process manipulations appeared to have an effect on the decision times they were not

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Figure 2: Distribution of decision times

Figure 3: Distribution of Log10 decision times

base.de.datos$RT

Frequency

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Histogram of base.de.datos$RTT

base.de.datos$RTT

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Figure 4: Mean contribution depending on trust induction condition

Afterwards, we examined whether the trust manipulation had a main effect on cooperation (see figure 4). A linear regression showed that the high trust induction

significantly predicts contribution rates (F(1, 293) = 4.20, p < .05, R2 = 0.014), suggesting the high trust manipulation increased contributions relative to the low trust manipulation. To demonstrate the robustness of this effect we conducted a series of additional analyses.

First, we explored the self-reported trust at moment of the decision (manipulation check) depending on the trust induction condition. Participants in the high trust condition reported a mean of 6.18 trust (SD= 2.52, N = 148), while participants in the low trust condition reported a mean of 5.36 trust (SD = 2.57, N = 147). A welch two sample t-test showed that this difference is significant (t(292.82) = -2.7745, p < .01), suggesting the high trust induction significantly increased participants’ perception of trust relative to the low trust induction.

Second, we showed that the self reported trust differs significantly from other emotions (see figure 5). The mean score for trust was 5.77 (SD = 2.57), the mean for anger was 2.08 (SD

= 1.88), the mean for happiness was 4.26 (SD = 2.75), the mean for sadness was 2.1 (SD = 1.85), the mean for gratitude was 4.35 (SD = 2.93), the mean for pride was 4.79 (SD = 3.03)

0 2000 4000 6000

0 1

Trust_manipulation

mean

Low High

Trust manipulation

Mean contribution (COP)

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and the mean for guilt was 3.1 (SD = 2.59). A set of paired sample t-tests showed that the self reported trust differed significantly from the other emotions: anger (t(294) = 18.14, p < .01), happiness (t(294) = 8.64, p < .01), sadness (t(294) = 18.01, p < .01), gratitude (t(294) = 8.04, p < .01), pride (t(294) = 5.27, p < .01), and guilt (t(294) = 11.18, p < .01). This suggests that the manipulations had a specific effect on trust rather than on other emotions.

Figure 5: Boxplots of self-reported trust and other emotions at the moment of the decision

Third, we determined the different trust induction conditions do not differ significantly in terms of pre-existing trust. Participants in the high trust condition reported a mean trust of 7.19 (SD = 1.73, N = 137), while participants in the low trust condition reported a mean trust of 6.93 (SD = 1.95, N = 138). A welch two sample t-test revealed that this difference is not significant (t(269.49) = -1.18, p > .05), suggesting the groups do not differ significantly in terms of pre-existing trust. This allows us to exclude the alternative explanation that the variation in contribution rates is not the result of the trust manipulation but of participant’s pre-existing trust.

Fourth, we demonstrated the randomization was successful. In particular, participants in the high and low trust conditions do not differ significantly in terms of experience with economic games (t(289.84) =-1.17, p > .05), faith in intuition (t(290.45) = -1.10, p > .05), comprehension of the game (t(292.95) =-0.52, p > .05), perceived awareness of the research hypothesis (t(288.77) =-0.28, p > .05), socioeconomic strata (t(291.99) =-0.82, p > .05), subjective socioeconomic status (t(290.33) = 0.61, p > .05), gender (t(291.98) = -0.47, p >

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Anger Happiness Sadness Trust Gratitude Pride Guilt

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.05), age (t(282.66) = s-0.34, p > .05) and knowledge of other participants (t(290.73) =-1.58, p > .05).

We then examined the moderating role of preference for information processing as measured by the REI-10. This scale is composed by the need for cognition (α = 0.36) and the faith in intuition (α = 0.62) sub-scales. A linear regression analysis was conducted to examine whether the interaction between the high trust induction and faith in intuition predicts

participants’ contributions. Results showed that the interaction predicts contribution (F(1, 293) = 4.845, p <.05, R2 = 0.016), suggesting that cooperation was higher among participants with a preference for an intuitive processing style assigned to the high trust induction (see figure 6).

Figure 6: Contribution depending on the interaction between trust induction and faith in

intuition

We also conducted a series of secondary analysis. We first examined the role of

beliefs. A linear regression showed that contribution rates were predicted by what participants’ believed other participants would contribute (F(1, 293) = 195.5, p < .001, R2 = 0.41),

suggesting participants who had positive expectations of others contributed more themselves. We then examined different measures of experience with economic games and research participation more generally. It is important to highlight that 79% of our subjects’ report having no experience with economic games, while 21% reporting having at least some

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Contribution depending on interaction between trust induction and faith in intuition

Trust induction*FI

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ution (COP)

Interaction between trust induction and faith in intuition

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experience. A series of linear regressions showed that contribution rates are not predicted by experience with economic games (F(1, 293) = 1.1, p > .05, R2 = 0.004), experience with research participation (F(1, 293) = 0.51, p > .05, R2 = 0.002), participation in paid

experiments (F(1, 293) = 0.32, p > .05, R2 = 0.001), participation in online experiments (F(1, 293) =0.22, p > .05, R2 = 0.0008) and participation in studies involving deception (F(1, 293) =0.05, p > .05, R2 = 0.0002). Thus, contribution levels appear not to be related to participants’ research experience. We also examined the relationship between behavior in the lab and in the real world as measured by social capital. A linear regression showed that social capital does not predict contribution rates (F(1, 293) =0.25, p > .05, R2 = 0.0008), indicating a lack of relation between contributions in the lab and real world social capital. Additionally, we examined whether demographic variables predict cooperation. A series of linear regressions showed that contribution rates are not related to age (F(1, 292) =0.0005, p > .05, R2 = 1.678e-06), gender (F(1, 292) =1.51, p > .05, R2 = 0.005), social strata (F(1, 292) =3.07, p > .05, R2 = 0.01) and subjective socioeconomic status (F(1, 292) =0.74, p > .05, R2 = 0.003). Finally, we show that contribution rates are not predicted by knowledge of other research participants (F(1, 292) =2.5, p > .05, R2 = 0.008).

We show that there are no significant demand effects3. The PARH scale is composed of 4 items (α = 0.71). The mean PARH value is 3.47 (SD = 1.4), which lies below the

midpoint (<4). Results from a one sample t-test on the mean PARH value showed that the mean score is significantly below the scale midpoint (t(294) = -6.5, p < .  001), indicating that participants reported, in general, feeling unclear about the research hypothesis. Additionally, results from a linear regression show that PARH scores do not predict contribution rates (F(1, 293) = 2.803, p > .05, R2 = 0.006), suggesting there is no relation between participant’s reported perception of the research hypothesis and the dependent variable.

Experiment 2

In experiment 1, we found that a behavioral manipulation of trust increased

cooperation. This effect appeared to be moderated by participants’ preference for information processing, with participants with a preference for an intuitive processing style assigned to the                                                                                                                

3 We followed Mark Rubin’s suggestions to run and interpret the data analysis:

https://sites.google.com/site/markrubinsocialpsychresearch/a-measure-of-the-influence-of-demand-characteristics

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high trust condition contributing more. This results provide support for the role of trust in the SHH. In experiment 2, we explored a different question: how do we adequately measure trust in daily life interactions?

Participants and design

104participants (58 men, 45 women and 1 other, average age = 22.21, SD = 5) participated in the experiment. They were also students from the Universidad Nacional de Colombia. The present study had a 2 (between subjects - cognitive process manipulation: time pressure vs. time delay) × 3 (within subjects – trust measures: TG -player type was between subjects, PTS & Trust Rand) design. Participants were randomly assigned to one of the 4 conditions: time pressure ×  trust measures (player 1 - TG), time pressure ×  trust measures (player 2 - TG), time delay ×  trust measures (player 1 -TG), and time delay ×  trust measures (player 2 -TG). The objective of the present experiment was to explore which of the different trust measures is a better predictor of intuitive cooperation.

The design was similar to experiment 1 with some differences. First, the trust

manipulation was not presented. Second, the REI-10 was not included to reduce the length of the experiment. Third, there was no pre-experimental survey because there was no

manipulation of trust. Therefore, the possibility that the self-reported perceptions of trust might be affected by the trust manipulation is no longer a concern. Instead, the self-reported perceptions of trust questions were included in the experiment with some filler items from the World Value Survey to conceal the purpose of the questionnaire. The order of the questions was randomized. Fourth, two trust measures were introduced: the TG and the PTS (see

measures section). The order of the games was randomized (TG and PGG) in order to examine whether the findings change depending on which game participants play first. Fifth, the

endowment in the PGG was reduced to $4,000 COP to keep it similar to the $4,000

endowment in the TG. Therefore, participants could win up to $10,000 COP in the PGG and $16,000 in the TG. However, all participants were paid the amount won in each game, there was no random payment. This is important given results from a TG meta-analysis showing that random payment reduces the amount that players 1 send to players 2 (Johnson & Mislin, 2011).

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Measures

• Trust Game: the game measures trust and trustworthiness using an experimental approach. In the game there are two players in different rooms. Each of the players is given $4,000 COP for their up. In the first stage, player 1 must decide how much of his show-up fee to send to player 2. Both players are told that the money that player 1 sends to player 2 will be tripled. In the second stage, player 2 receives the tripled money and decides how much of the money to keep and how much to return to player 1. Trust is measured as the amount of money transferred from Player 1 to Player 2 (Berg et al., 1995). After the game we included some questions to measure comprehension of the task and justification for the amount sent/returned.

• Propensity to Trust Survey: this 21-item self-report instrument measures trust and trustworthiness using an individual differences approach. The instrument adopts a definition of trust that fits within those that underscore expectations in situations where individual interests are at odds with collective interests. As evidence for its utility, the trust and trustworthiness scales are reliable and related to several of the Big Five personality traits. In addition, the instrument predicts the behavior of agents in a TG, better than the corresponding Big Five traits (Evans & Revelle, 2008). The questions were presented with some filler items from the Big Five (John & Srivastava, 1999) to hide the purpose of the questionnaire. The order of the questions was randomized.

Procedure

The procedure was similar to experiment 1. However, there were a few differences. First, participants could be recruited face to face as well as by email because they didn’t have to fill a pre-experimental survey. Second, participants could win a maximum of $34,000 COP ($10,000 from the PGG, $14,000 from the TG and $8,000 from the show-up fee) instead of $26,000 COP. This experiment did not involve random payment. The show-up fee was raised because the experiment was longer (approximately 40 minutes). Third, the protocol was different depending on whether we were running players 1 or 2 in the TG. Running a player 1-session was easy as it was all automated in Qualtrics. However, when running a player 2-session we had to inform each participant in a separate format the amount of money they received from players 1. Fourth, participants were not paid at the end of the experiment but at

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the end of the week. The reason for the delay is we didn’t have two rooms to run the TG sessions simultaneously (players 1 and players 2 in the TG cannot be in the same room). Finally, we took several preventive measures to avoid recruiting the same participants from experiment 1: we asked participants to avoid participating if they had participated in

experiment 1, we recruited participants from other faculties, and we introduced a control question at the end of the experiment. Additionally, we checked the personal information to see if it matches with that of experiment 1. A summary of the study and the order of the tasks can be found in figure 7.

Experiment Structure Payment

PGG under TP or TD (random), belief, understanding, justification

TG - player 1 or player 2, comprehension, justification

PTS

(randomized)

Trust questions (randomized)

Experience, social capital,

PARH Demographics

At the end of the week order of games randomized

Figure 7: Summary of experiment 2

Results

In experiment two, we examined which trust measure is a better predictor of intuitive cooperation. In the TG trust is measured as the amount sent from player 1 to player 2, in the PTS trust is measured by the trust scale (α = 0.76) and in the self-reported perceptions, trust is measured using the questions used previously by Rand et al. (2012). We first examined some descriptive statistics. Players 1 in the TG sent a mean of $2,695 COP to players 2 (SD =   1237.49, N =  56), participants in the PTS had a mean score of 3.66 (out of 6) in the trust scale of the PTS (SD = 0.92, N =  104) and participants reported a mean score of 7.21 (out of 10) regarding their trust in daily life interactions (SD =  1.83, N =  104). Concerning comprehension of the games, 38% failed and 62% passed in the PGG, while 57% failed and 43% passed in the TG. However, in the TG we asked four instead of two comprehension questions and the questions were more complicated. If we assume that people who answered three out of four questions correctly comprehend the TG we get that 41% failed and 59% passed the

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contribution levels (F(1, 102) =0.41, p > .05, R2 = 0.004) and trust (F(1, 54) =1.15, p > .05, R2 = 0.021) in the PGG and the TG, respectively.

We then analyzed whether each of the trust measures is able to predict intuitive cooperation. A series of linear regressions shows that the interaction between time pressure and the different measures is not significant: TG ×  time pressure (F(1, 54) =2.67, p > .05, R2 = 0.047), PTS ×  time pressure (F(1, 102) =1.44, p > .05, R2 = 0.014) and self-reported trust ×   time pressure (F(1, 102) =1.29, p > .05, R2 = 0.012). However, when we examined the

principal effect of each trust measure the results are different. In particular, the amount sent by players 1 in the TG (F(1, 54) =16.74, p < .001, R2 = 0.24) and the self-reported perception of trust (F(1, 102) =6.99, p < .01, R2 = 0.064) significantly predict contribution rates. Still, the trust scale of the PTS does not predict contribution (F(1, 102) =0.73, p > .05, R2 = 0.007). When we conducted a multiple regression model with the three trust measures we found a similar result (F(3, 52) =9.58, p < .001, R2 = 0.3559). However, the amount sent by player 1 in the TG is a better predictor (b = 0.47) than the self-reported trust (b = 0.38).

We now demonstrate that this finding is not explained by order effects (Brañas & Barreda, 2011). In order to show this, we conducted a series of linear regressions to examine whether the TG predicts contribution rates with two different data sets: when players play the TG and then the PGG, and when players play the PGG and then the TG. Results show that in both cases the TG predicts contribution rates: TG-PGG (F(1, 26) =12.62, p <.01, R2 = 0.33) and PGG-TG (F(1, 26) =5.766, p < .05, R2 = 0.18). This shows that the findings are not explained by order effects.

We then examined whether our results support the SHH. On the one hand, participants in the time pressure condition contributed a mean of $2,490 COP (SD =  1139.06, N =  53) and participants in the time delay condition contributed a mean of $2,236 COP (SD =  1321.58, N =   51). While the direction of this results is consistent with the SHH, a welch two sample t-test revealed that this difference is not significant (t(98.59) = -1.05, p > .05). Still, the cognitive process manipulations appear to have an effect on the decision times. Participants under time pressure responded in a mean time of 13.94 seconds (SD = 6.67, N = 53), while participants under time delay responded in a mean time of 28.85 seconds (SD = 14.64, N =51). A welch two sample t-test reveals that this difference is significant (t(69.313) = 6.64, p <.001), suggesting the cognitive process manipulations had an effect on decision times. We then

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analyzed the distribution of the decision times and find that it is right skewed. Therefore, we applied a log10 transformation. Afterwards, we repeated the welch two sample t-test and the results were similar (t(99.33) =6.94, p <.001). However, when we conducted a linear

regression with log10 decision times as predictor of contribution rates, results showed they are not significantly related (F(1, 102) =3.131, p >.05, R2 = 0.03). This suggests that decision times do not significantly predict contribution rates. On the other hand, participants with experience with economic games contributed a mean of $2,470 COP (SD = 1272.2, N = 26), while naïve participants contributed a mean of $2,331 (SD = 1225.4, N = 78). The direction of this result is inconsistent with the SHH. However, a welch two sample t-test revealed that this difference is not significant (t(41.56) = 0.49, p > .05).

We also conducted a series of secondary analysis. We first examined the role of beliefs. A linear regression shows that contribution rates are predicted by what participants’ believed others would contribute (F(1, 98) = 109.6, p < .001, R2 = 0.528), indicating participants who had positive expectations of others’ contributed more themselves. We also examined different measures of experience with economic games and research participation more generally. It is important to highlight that 75% of our subjects’ report having no experience with economic games, while 25% reporting having some experience. A series of linear regressions showed that contribution rates are not predicted by experience with economic games (F(1, 102) = 0.81, p > .05, R2 = 0.0079), experience with research

participation (F(1, 102) = 0.191, p > .05, R2 = 0.0019), participation in paid experiments (F(1, 102) = 0.031, p > .05, R2 = 0.0003), participation in online experiments (F(1, 102) = 0.003, p > .05, R2 = 2.957e-05) and participation in studies involving deception (F(1, 102) = 2.44, p > .05, R2 = 0.02). Thus, contribution levels appear not to be related to participants’ research experience. Additionally, despite the checks implemented, 12% of our subjects reported having participated in our previous experiment. However, contribution levels are not predicted by participation in experiment 1 (F(1, 102) = 0.003, p > .05, R2 = 2.984e-05). We also

examine the relationship between behavior in the lab and in the real world as measured by social capital. A linear regression showed that social capital predicts contribution rates (F(1, 102) =  11.26, p< .001, R2 = 0.099), indicating participants with higher levels of social capital cooperated more in the lab. Additionally, we examined whether demographic variables predict cooperation. A series of linear regressions showed that contribution rates are not related to age

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(F(1, 102) =  0.07, p > .05, R2 = 0.0007), social strata (F(1, 102) =  0.039, p > .05, R2 = 0.0004) and subjective socioeconomic status (F(1, 102) =  0.03, p > .05, R2 = 0.0003). However, gender significantly predicted cooperation (F(1, 102) =1.51, p > .05, R2 = 0.005), suggesting men contribute more than women. Finally, we show that contribution rates are not predicted by knowing other research participants (F(1, 102) =  0.3, p > .05, R2 = 0.003).

Finally, we show that our findings are not explained by demand effects. The PARH scale is composed of 4 items (α = 0.73). The mean PARH value is 3.4 (SD =  1.45), a value that lies below the midpoint (<4). Results from a one sample t-test on the mean PARH value show that the mean score is significantly below the scale midpoint (t(103) = -4.11, p < .  001), indicating that participants reported, in general, feeling unclear about the research hypothesis. Additionally, results from a linear regression show that the PARH scores does not predict contributions (F(1, 102) = 0.42, p > .05, R2 = 0.004), suggesting that there is no relation between participant’s reported perception of the research hypothesis and the dependent variable.

Discussion

The present project examined the role of trust in the SHH. In particular, we studied the impact of a behavioral manipulation of trust on intuitive cooperation and explored multiples measures of trust. In addition, we tested an individual difference moderator: preference for information processing.

In experiment 1, we found that a behavioral manipulation of trust increases

cooperation. The robustness of this effect was confirmed by convergent evidence. First, the manipulation check demonstrates that participants in the high trust condition report more trust at the moment of the decision than participants in the low trust condition. Second, the trust manipulation appears to have a specific effect on trust rather than on other emotions. Third, this finding is not explained by participants’ pre-existing levels of interpersonal trust, as the different trust induction conditions do not differ in this respect. Fourth, the randomization appears to be successful. Fifth, the findings are not explained by demand effects. However, the effect of the trust manipulation does not depend on the cognitive process involved, either intuition or reflection. Still, the cognitive process manipulations appear to have an effect on decision times.

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Moreover, the effect of the trust manipulation on cooperation appears to be moderated by participants’ preference for information processing. Specifically, participants with a preference for an intuitive processing style assigned to the high trust condition tend to

contribute more. Furthermore, cooperation appears to be explained by expectations about the contribution of other participants, with participants with positive expectation of others contributing more themselves. However, experience with economic games and research participation more generally does not explain participants’ level of cooperation. In addition, cooperation in the lab appears to be unrelated to real world prosocial behavior.

In experiment 2, we found that the TG was the best predictor of cooperation, followed by self-reported trust and with the PTS not being a significant predictor. Therefore, an

experimental measure appears to be the best way to measure trust. We found that this result is not due to order or demand effects. Unexpectedly, the measures did not depend on the

cognitive process at play. While we found that participants contribute more under time pressure than under time delay, the difference is not significant. However, the cognitive process manipulations appear to have an effect on decision times. Regarding experience, participants with experience with economic games contribute more than naïve participants, though the difference is not significant. Cooperation did not appear to depend on more general research participation. However, we found that cooperation is explained by expectations about the contribution of other participants, with participants with positive expectation of others contributing more themselves. We also found that real world prosocial behavior predicts cooperation in the lab. Finally, men appear to be more cooperative than women.

Our results have important implications for the SHH. The effect of the trust

manipulation on cooperation provides causal evidence of the importance of trust in this model. This finding is important as previous studies have tested the effect of exposing participants to environments that support or undermine cooperation (Peysakhovich & Rand, 2013) without directly manipulating interpersonal trust. Additionally, we show that this effect is moderated by participant’s preference for information processing, suggesting that having trusting in daily life interactions has a stronger influence on cooperation among participants who have an intuitive processing style. Previous studies have shown that experience with economic games makes subjects less reliant on their intuitions (Rand & Kraft-Todd, 2014c). Here, we show that interpersonal trust has a stronger effect on cooperation among subjects who tend to follow

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their intuitions. Our result adds up to a growing literature demonstrating the relevance of trust in the SHH (Peysakhovich & Rand, 2013; Rand & Kraft-Todd, 2014c; Capraro & Cococcioni, 2015).

The fact that this effect does not depend on the cognitive process manipulations is inconsistent with the SHH, which posits that cooperative norms developed under trusting daily life interactions spill to unrelated domains when making decisions under time pressure but not under time delay (Rand et al., 2014a). However, our results suggest -instead- that having trusting daily life interactions increases cooperation via individual differences in information processing preference rather than by manipulations of cognitive processing. Thereby,

supporting the role of intuition in the SHH. Still, this result should be interpreted with caution as it is possible that the trust inductions could have overridden the effect of the cognitive process manipulations. Nonetheless, results from experiment 2 show the cognitive process manipulations do not have a significant effect on cooperation in absence of trust

manipulations. Still, the direction of the result is consistent with the SHH, without being significant. Future studies should try to disentangle the precise relationship between trust and intuition. Additionally, experiment 2 fails to show a moderating role of experience with

economic games. Therefore, results from experiment 2 fail to replicate the role of intuition and experience, and add up to a number of studies that have failed to support the SHH (Tinghög et al., 2013, study 5; Verkoeijen & Bouwmeester, 2014, study 3).

We also show that an experimental measure trust is a better predictor of cooperation than self-reported perceptions of trust. This suggests that trust in daily life interactions is better captured through incentivized experiments in which participants face the decision to trust (or distrust) instead of self-report measures which may be limited in many respects (Baumeister et al., 2007). Still, consistent with previous studies (Rand et al., 2012; Rand & Kraft-Todd, 2014c) our results show that self-reported trust is a significant predictor of cooperation. Unexpectedly, the trust measures did not depend on the cognitive process at play. This suggests that having trusting daily life interactions has a direct effect on cooperation,

independently of the cognitive process manipulations. This raises further doubts about the role of intuition in the SHH.

Furthermore, our results offer mixed evidence about the relation between cooperation in the lab and real world prosocial behavior as measured by social capital. While in

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experiment 1 social capital and cooperation were unrelated, in experiment 2 participants with higher social capital contributed more. While we consider social capital is a suboptimal

measure of real world cooperation -as it does not directly measure cooperation, the question of external validity of the SHH is of great importance (see Levitt & List, 2007) and particularly worrisome in light of recent findings showing a mismatch between findings from the lab and the field in the domain of social preferences (e.g. Winking, & Mizer, 2013; Galizzi &

Navarro-Martinez, 2015). Future studies should explore the external validity of the SHH by conducting a lab-field study (e.g. Galizzi & Navarro-Martinez, 2015) in which variables from the lab are operationalized in a field situation with greater realism.

Moreover, our experiment suggests potential interventions to increase cooperation in the field. In particular, an intervention that primes subjects to remember a situation in which trusting other people led to positive consequences in their daily life’s could be effective. This prime could be presented in situations in which participants face a real-world cooperative dilemma such as conserving the environment or donating money to charities. The intervention would be expected to be effective given recent findings showing social interventions

consistently increase real world cooperation. Specifically, our intervention fits into those based on descriptive norms, insofar as the prime is designed to remind participants of

situations in which other people acted cooperatively in the past (Kraft-Todd, Yoeli, Bhanot, & Rand, 2015).

Finally, there are some limitations in our studies. First, our sample from experiment 1 consists primarily of undergraduate students. Results show that students are less altruistic than non-students (Engel 2011). Therefore, it is necessary to extend our findings to a non-student population. Second, in experiment 1 we paid only one out of every four participants the corresponding amount of money won in the game. PGG meta-analysis (e.g. Zelmer, 2003) have not examined the effect of random payment on contribution levels. However, TG meta-analysis has shown that random payments reduces trust (Johnson & Mislin, 2011). Whether or not our random payment scheme altered participants’ cooperative behavior (e.g. by adding additional risk) is still an open question that warrants further attention. Third, in experiment 2 we found that contribution levels in the PGG were predicted by trust in the TG. Although we were able to control for order or demand effects we were not able to exclude consistency effects. Therefore, it is possible that the relation between trust and cooperation is explained by

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participants’ behaving similarly in both games. Future studies should run both games in separate sessions to exclude this possibility.

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