However, some cooperative play is observed, particularly at the earliest stages, in numerous experimental tests with this game (Andreoni and Miller, 1993; Cooper et al., 1996; Pothos et al., 2011, among others). One way to reconcile the theory with the experimental evidence is to assume some kind of incomplete information. If one player does not know the true payoffs of the opponent, for example, and assigns a positive probability that the other will not defect, mutual cooperation can be sustained as equilibrium (Kreps et al., 1982). One possible interpretation of the cooperation observed in experimental games, then, is that some players are "altruistic," in the sense that their true payoffs from cooperation are greater than the given monetary ones, and players' types are not common knowledge. Cooperation thus would be played by altruists. In a repeated game, an alternative explanation is that some players may try to "build a reputation" of cooperation in order to achieve a higher total payoff in the game. Both Andreoni and Miller (1993) and Cooper et al. (1996) use evidence of cooperation in the one-shot PD game as an indicator that a positive proportion of individuals are actually altruistic1 . They also find that cooperation is higher when the PD is repeated for a finite number of times, consistent with reputation building. Players then turn to defection toward the end of the game, even if at a slower pace than predicted by Kreps et al. (1982). In their work, altruism is only a hypothesis to explain cooperative behavior, given that no independent measure is used to classify subjects as altruistic. However, even if altruists are expected to cooperate more, cooperation and altruism are not the same thing. Following Dreber et al. (2014) and Capraro and Marcelletti (2014), in our experiment we use as a treatment variable an exante measure of altruism: giving in a Dictator Game. That is, we define as altruism the willingness to sacrifice one's own payoff in order to increase the other's payoff. Furthermore, we elicit subjects' beliefs in the PD games in order to better understand the relationship between altruism and cooperative behavior.

Several laboratory experiments have been conducted to analyze whether the cooperators in a repeated Prisoner's Dilemma (RPD) can be identified by some kind of measurable characteristics. Dreber et al. (2014) find that altruism leads to more cooperation in a noisy version of the infinitely repeated PD game only if no cooperative equilibrium exists. However, altruism does not play any role in determining the outcome when cooperation can be sustained in equilibrium. Their results support the view that social preferences are not important predictors of cooperation. Rather, individuals seem to cooperate mainly driven by payoff maximization motives. Using a dictator game to measure altruism and a standard PD game to measure cooperation, Capraro and Marcelletti (2014) find that being recipient of an altruistic act does not increase your probability of being cooperative with a third party.

We analyze the effect of altruism on cooperation, defined as the willingness to increase the joint payoffs of yourself and the other, which can be observed using one-shot and finitely repeated PD games. According to the Social Value Orientation (SVO) literature, prosocial individuals tend to maximize outcomes for both themselves and others (Van Lange et al., 1997). Eliciting beliefs about partner's cooperation allows us to tell apart whether participants classified as altruists in our study cooperate conditionally, i.e., based on the expectation that the other will also cooperate, or unconditionally, that is, even if thinking that the other will defect2 .

Individuals' cognitive ability/intelligence has also been associated with cooperative play. One natural supposition is that more intelligent individuals should make more "rational" choices, exhibiting behavior consistent with game theoretic predictions, such as the sub-game perfect Nash equilibrium (Burks et al., 2009; Proto et al., 2015). Accordingly, these individuals should be observed to cooperate less in both oneshot and finitely repeated PD games. The empirical evidence, however, does not seem to support this conjecture. For instance, using a meta-study of repeated (PD) experiments run at numerous universities, Jones (2008, 2013) suggests that the average intelligence of game participants should be considered among the most robust factors driving individual cooperation. Specifically, this author finds that students at schools with higher average scores in the Scholastic Assessment Test (SAT) and the American College Test (ACT) tended to cooperate more often in a RPD3 . Using a sample of 1,000 truck driving students in a one-shot sequential (PD), Burks et al. (2009) find that subjects with higher IQ more accurately forecast others' decisions and differentiate their actions more strongly given the first-mover's choice, exhibiting behavior that is far from the sub-game perfect equilibrium of the game.

Other experimental studies find mixed evidence regarding the conjecture about the negative link between cognitive ability and cooperative play. Yamagishi et al. (2014) find that decisions coherent with the maximization of self-interest are linked indeed to higher IQ. However, psychological assessment of the participants in their study leads to the conclusion that those classified as "Homo Economicus" might behave in a selfish

1 In Cooper et al. (1996) a player is classified as an altruist (egoist) if he plays cooperatively more (less) than 50% of the time in 20 one-shot PD games. Andreoni and Miller (1993) consider any cooperation in the one-shot game as a sign of altruism.

2According to the SVO literature, prosocial individuals can be either altruists (unconditional cooperators) or cooperators (conditional cooperators). Differently from this literature, we do not identify altruists as unconditional cooperators.

3 Jones (2014) increases the information processing necessary to implement strategies supporting cooperation of the RPD game through random switching between permutations of the payoff table. This additional strategic complexity attenuates the relationship between cognitive ability and cooperation observed in Jones (2008). In a related cognitive load experiment, Duffy and Smith (2014) find that a decrease in the cognitive load of subjects increases strategic defection near the end of the RPD game.

manner only in a situation in which no future consequence of their choice is expected. These subjects can better assess the future and adopt long-term strategies. In the same line, using an experimental design similar to ours but with just one factor (participants are allocated into two groups according to their level of intelligence), Proto et al. (2015) find that higher intelligence groups do not cooperate more in the initial rounds of an infinitely repeated PD game, but seem to learn better how to reciprocate their partner's behavior over time. However, there are no significant differences in the same design with lower continuation probability. Also recently and in contrast with Jones (2008, 2013), Al-Ubaydli et al. (2016) find that cognitive ability does not predict individual cooperation in a 10-round PD game but paired cooperation is positively correlated with the average cognitive ability of the two players. In their study, individuals with higher cognitive abilities reciprocate cooperation in the second round of the PD game significantly more than low cognitive ability subjects, like in Burks et al. (2009).

Given the previous findings, an alternative conjecture is then that more intelligent individuals better adapt to the circumstances in strategic situations4 .

Our objective in this paper is to test the significance of the joint effect of cognitive ability and altruism on cooperative behavior in a series of one-shot and finitely repeated PD games. In order to do so, both characteristics are implemented as treatment variables, separating individuals in four distinct groups based on the interaction of their high/low level of cognitive ability (measured with the Differential Aptitude Test on Abstract Reasoning), and their high/low altruistic giving in a Dictator Game (DG). In the aforementioned literature, altruism or cognitive ability or both are treated as control variables rather than treatment variables, or not taken into account. Our 2 by 2 factorial design matches individuals with similar cognitive ability and level of altruism, allowing us to neatly observe the effect of these factors on cooperation. In other words, the effect of a high reasoning ability individual with high altruism might get diluted if she found for instance a low intelligence low altruism partner when playing a RPD. Our study tries to avoid this problem.

Subjects belonging to each group played 10 one-shot PD games and three 10-round repeated PD games where we elicited players' beliefs using an incentive compatible method. Our paper is the first introducing players' beliefs to analyze expectations and behavioral rules in the RPD game under different treatments of altruism and reasoning ability.

Based on the previous review, in our study we propose the following hypotheses:

#### **Hypothesis 1:** High altruism individuals should cooperate more in both one-shot and repeated PD.

Given our definition, an altruist should be willing to increase the other's payoff at the cost of decreasing her own expected payoff, which is exactly what happens when an individual chooses the dominated cooperative strategy in our PD games.

**Hypothesis 2:** Individuals with higher cognitive ability should more accurately forecast their partner's actions in both types of games (one-shot and repeated), and thus be able to differentiate their behavior accordingly.

We assume that making better predictions is a necessary precondition to adapt successfully to a strategic situation. In line with Proto et al. (2015), we consider that more intelligent individuals should be capable of better assessing and adapting to the environment. Thus, they should better realize the scope for reputation building in the repeated game as opposed to the one-shot game.

**Hypothesis 3:** Reasoning ability should counteract the effect of altruism in the one-shot game, while it should reinforce it in the repeated PD game.

Our first two hypotheses propose that, while altruism should always increase cooperation, reasoning ability should lead to increased or decreased cooperation depending on the circumstances. This implies a non-linear interaction between the two factors.

Our results confirm the two first hypotheses using a clean experimental design. Reasoning ability is found to indeed counteract the effect of altruism in the one-shot games, but to reinforce it only in the first RPD. In general, the effect of the individual characteristics on the cooperation decision fades out with the repetition of the RPD game.

The article is organized as follows: Section Methods describes the experimental design and Section Results presents the results. Section Discussion discusses the results and concludes.

# METHODS

We turn to experimental economics methodology to create a controlled, saliently motivated and replicable environment in which to test our hypotheses. As a first step, we used an experimental setting to measure our subjects' reasoning ability and altruism. After creating four different groups according to the results of these measures, we invited again the same subjects to the lab for a different experiment. In this second step, subjects were randomly paired with other subjects of similar reasoning ability and altruism, without them knowing this information, and played four sets of (PD) games both one-shot and repeated. Thus, each subject whose data we present in this study has participated in two sessions in different days of two consecutive weeks in December 2014: all sessions of the second experiment were carried out during the week after the last session of the first experiment. As the participants did not receive any payment up to the end of the second session, the attrition rate was low: out of 178 subjects who participated in the first set of sessions, only 16 did not participate in the second set of sessions. Subjects were recruited among undergraduate students from different degrees at Universitat Jaume I (Spain), using ORSEE (Greiner, 2015). At the beginning of each session, subjects were given written

4 Intelligence and adaptive behavior are found to be separate but related constructs exhibiting low to moderate correlations depending on the particular measures (Harrison, 1987; Keith et al., 1987; Platt et al., 1991). The underlying mechanism behind the relationship between intelligence and adaptive behavior is out of the scope of our paper.

instructions, which were also read aloud by the organizers. Any remaining questions were privately answered.

At the end of the second session, subjects found out their actual gains and were privately paid in cash the total amount obtained in both sessions. Average earnings were around 11e for the first experiment and around 14e for the second one, and the sessions lasted 1 and 1 h and a half, respectively. Experiments were computerized and carried out in a specialized computer lab (LEE at Universitat Jaume I), using software based on the Z-Tree toolbox by Fischbacher (2007).

Each of the two experimental designs is described in detail in the following subsections. Experimental instructions can be found in Section 1 of the Supplementary Material.

# Testing for Reasoning Ability and Altruism

In the first experimental setting, subjects were asked to complete two tasks. The first task consisted in completing the Abstract Reasoning part of the Differential Aptitude Test for Personnel and Career Assessment (DAT-AR for PCA, Bennett et al., 1974). The Abstract Reasoning (AR) scale of the DAT used in this experiment is included in the DAT-5 Spanish adaptation by the publisher TEA (Cordero and Corral, 2006). This test is usually used as a non-verbal measure of reasoning ability and involves the capacity to think logically and to perceive relationships in abstract figure patterns. It is considered as a marker of fluid intelligence (Colom et al., 2007), the component of intelligence most related to general intelligence or g factor (McGrew, 2009). The advantage of this test is that it is quite fast to implement: it is comprised of 40 multiple-choice items and has a 20 min time limit. Subjects were informed that they would receive 0.25e for each right answer.

The second task included a Dictator Game where each subject played both as dictator (which we more neutrally called "sender") and recipient, and then was randomly assigned one of the two roles. An endowment of 10e was provided to dictators, who could transfer any amount from 0 to 10e to their respective anonymous recipient in increments of 0.1e. Subjects were informed that in this task the recipient would receive no payment other than the one they chose to give. In our analysis we use the amount given in the dictator game as a measure of subjects' altruism. The dictator game is positively correlated to altruistic acts in real-life situations (returning money to subjects in Franzen and Pointner (2013) using the misdirected letter technique), charitable giving (Benz and Meier, 2008) and willingness to help in a real-effort task (Peysakhovich et al., 2014). Additionally, Carpenter et al. (2008) find that the specific survey questions for altruism used in their study are positively correlated with DG giving. Using a related concept, Capraro et al. (2014) find benevolence to be correlated with cooperative behavior, but their definition of benevolence "to increase the benefit of someone else beyond one's own" has no cost to the "benevolent" player. We consider that a person acts altruistically if she unilaterally pays a cost c ≥ 0 to increase the benefit of someone else. More formally, Player 1 is altruist toward Player 2 if she prefers the allocation (x1-c, c) to the allocation (x1, 0), where c > 0. The larger the c, the more altruist we consider this subject to be.

After completing the aforementioned tasks, subjects were divided in four groups according to their reasoning ability and altruism and called again to the lab. Apart from 16 who decided not to continue with the second session and just came separately to the lab to get their gains in the first session, the rest continued. A subject was classified as "high altruism" if she chose to transfer more than the median transferred amount in the dictator game, and as "high reasoning" if her score was higher than the median score in the DAT-AR test. Following this classification, the final four treatment groups are named "Low Altruism and Low Reasoning" (LALR, 42 subjects), "Low Altruism and High Reasoning" (LAHR, 46 subjects), "High Altruism and Low Reasoning" (HALR, 42 subjects) and "High Altruism and High Reasoning" (HAHR, 32 subjects). Therefore, a total of 162 subjects (81 pairs of players) took part in the PD sessions. Subjects were not aware at any point of the existence of the four treatments. We could not control the gender composition of each treatment but it turned out quite balanced, always in the 60–40% of females range. In **Table 1** we summarize the treatments implemented.

# PD Games

We organized 8 PD sessions, 2 for each treatment group. Each PD session began with training questions on the PD to make sure that players fully understood the mechanism of the game. Then, subjects belonging to the same treatment group were faced with four consecutive PD tasks. Subjects were informed that they would be paid according to their decisions in only one of the four tasks, randomly selected at the end of their session.

# One-Shot PD Games

The first task consisted in a sequence of 10 one-shot PD games against potentially different anonymous opponents using a strangers-pairing mechanism. No player knew the identity of the player with whom she was currently paired or the history of decisions made by any of the other players.

**Table 2** shows the payoffs of the one-shot PD game. In each cell, the first (second) figure denotes the payoff in euros of player 1 (2). Clearly from the Table, "A" represents the decision to cooperate and "B" not to cooperate.

TABLE 2 | Payoffs of the one-shot game.

In order to avoid endowment effects across the one-shot games in this task, we used the RLI (Random Lottery Incentive) system as payment mechanism. That is, if this task was selected for payment, only one randomly drawn PD game was remunerated. We didn't randomize task order and made all players play this task first, so that subjects could face a great number of opponents (up to 10 different ones) and in this way get some information about the population of players that they were facing.

#### Finitely Repeated PD Games

In the last three tasks participants played a repeated PD game, in which each subject played 10 rounds of the same game with a given participant using a partners-pairing mechanism. Therefore, each subject played 10 consecutive rounds with the same opponent. Players were then anonymously re-matched with new opponents and played a new RPD lasting again 10 rounds. At the end of each period in a repetition, subjects were shown what their opponent had played. However, when players were rematched, they were not told anything about the history of play of their new opponent.

The payoffs of each round for all three RPD tasks are shown in **Table 3**. It can be observed that they are just equal to those of a round of the one-shot game divided by ten.

## Beliefs

In order to gather more detailed information on players' strategic reasoning, subjects were asked the following questions before each round of each game:

With the first question we elicit the "individual" belief and with the second one the "social" belief on individual cooperation.

Subjects could earn up to two additional euros for these questions, according to their answers5 .

# RESULTS

Before reporting the detailed results related to cooperation behavior in the (PD) tasks, we first describe the outcomes of the reasoning ability test and of the Dictator Game, and subjects' beliefs in the PD tasks.

# Descriptive Statistics

**Figure 1** presents the distribution of the number of observed correct answers to the 40 multiple choice items in the DAT-AR test. The mean and the median number of right answers were 23.9 and 24 out of 40, respectively, and the standard deviation was 6.7. Mean and median number of correct answers are almost

#### TABLE 3 | Payoffs of the RPD game.

identical to the ones calculated for the Spanish population of a comparable age (Cordero and Corral, 2006).

**Figure 2** shows the distribution of the transfers in the Dictator Game. About 80% of our subjects gave non-zero amounts. The mean and median transfer were of 2 and 1.4e out of 10e, respectively, and the standard deviation was almost 2e. Comparing these results with the range of outcomes in the dictator game meta-analysis of Engel (2011), our values are within the range of what is typically observed (dictators on average give 28.35% of the pie).

**Table 4** shows descriptive statistics on reasoning ability and altruism for subjects included in the four treatment groups. On average, "high" altruism subjects transfer about 3e more than "low" altruism ones, while subjects with "high" reasoning ability answered correctly to about 10 additional questions with respect to subjects with "low" reasoning ability. Comparing these results with the general ones for Spain from Cordero and Corral (2006), 19 correct answers correspond to about the 25% percentile of the DAT-AR scores distribution, and 29 correct answers to about the 75% percentile.

For the pooled data, there is a significantly negative correlation between altruism and reasoning ability, but it is quite low (Spearman's rho of −0.17, p = 0.032). Besides, the correlation between the two characteristics is not significant within each group. However, we test for collinearity in our regression analysis.

## Beliefs

**Figure 3** shows the percentage of participants whose belief is that their partner will cooperate in that particular period (the

5At each round of the one-shot PD, subjects received 1e for answering the first question correctly and 1e minus as many cents as the difference (in absolute value) between their answer to question 2 and the actual percentage of players choosing cooperation in that round. At each round of the repeated PD the stakes were one tenth of the one-shot PD, that is 0.1e gain, and one tenth of the difference penalty.

TABLE 4 | Altruism (A) and Reasoning ability (R) descriptive statistics by treatment.

"individual belief," that is, the answer to question 1 reported in Section Beliefs above) by task, period and treatment. In the oneshot game high altruism individuals with low reasoning ability (HALR) have a higher expectation of partner cooperation than the rest. This difference is significant for the first seven periods when we compare HALR vs. LALR (with the exception of period 6) and HALR vs. LAHR using a proportion test, and for the first period when we compare HALR vs. HAHR. The full test statistics are presented in Table SM2.1 in the Supplementary Material (all our tests p-values have been Bonferroni corrected to take into account the problem of false positives in multiple comparisons).

In the first period of each RPD task we observe that HALR individuals continue to have the most positive expectations about partner cooperation, while LAHR subjects are the most pessimistic, this difference being significant for tasks 2, 3, and 4 (see the proportions tests results in Tables SM2.2–SM2.4 in the Supplementary Material). However, these treatment differences level off over time within each RPD game.

On average over all periods in a task, high reasoning ability subjects have a lower expectation of partner's cooperation in the one-shot game (Mann-Whitney test z = −4.034 and p = 0.0001), while there are no significant differences in expectations in the repeated PDs. This shows that HR individuals' beliefs are more consistent with the Nash equilibrium of the game, but only in the one-shot.

The mean percentage of individuals expected to cooperate in each period (the "social belief," that is, the answer to the second question reported in Section Beliefs), shows a similar pattern to that of the individual belief (see Figure SM2.1 in the Supplementary Material).

The elicitation of beliefs allows us to measure the number of individuals who have correctly guessed their partner's behavior in any given period, that is, they expected cooperation and the other has indeed cooperated, or they expected defection and the other has defected. Dividing this number by the total number of individuals in the treatment, we obtain the percentage of correct beliefs for each task, period and treatment (presented in **Figure 4**). According to Hypothesis 2 in the Introduction, we should observe that individuals with higher cognitive ability better forecast their partner's behavior. The percentage of correct individual beliefs is significantly higher for high reasoning ability subjects in the first four repetitions of the one-shot game (see Table SM2.5 in the Supplementary Material) and in the first period of task 2. In particular, LAHR participants reach 100% accuracy in almost half of the periods in all tasks, more often than the other treatments. However, there are no systematic differences in the remaining periods and tasks (Tables SM2.6– SM2.8 in the Supplementary Material). In the RPD tasks, the percentage of correct guesses is above 80% for most periods, for all treatments.

**Result 1:** High cognitive ability subjects better forecast their partner's behavior in the first repetitions of the one-shot games and at the beginning of the first RPD. However, there are no systematic differences in the percentages of correct guesses in the remaining repetitions of the RPD.

Notice that high altruism individuals with low reasoning ability less accurately forecast their partner's behavior in task 1. This is consistent with the fact that they have a too optimistic view of their partner's behavior in the one-shot game.

# Individual Cooperation in Period 1 of Each Task

In **Figure 5** we present the percentage of subjects choosing to cooperate in period 1 for each task and treatment.

The observed level of cooperation in the very first one-shot PD game depends on both altruism and reasoning ability.

**Result 2:** In the first PD game altruism tends to increase cooperation while reasoning ability tends to decrease it.

Coherently with our Hypotheses 1 and 3, in the first oneshot PD game high altruism subjects cooperate more than low altruism subjects, and high reasoning ability subjects cooperate less than low reasoning ability ones. These differences are significant using a proportion test, as reported in SM2.12 (period 1).

**Result 3:** Individual cooperation rates are higher at the beginning of RPD games than at the beginning of the sequence of one-shot PD games, particularly for high reasoning ability subjects.

Using a proportion test we obtain that the percentage of individuals cooperating in period 1 is significantly higher in all repeated PD tasks than in task 1 for all treatments with the exception of the HALR treatment (see Table SM2.9 in the Supplementary Material). After a significant increase in first period cooperation from task 1 to task 2 especially for high reasoning ability subjects, the cooperation level remains stable at the beginning of the remaining tasks. Consistently with our Hypothesis 2, we observe a more marked difference in behavior between the one-shot and the repeated tasks for high reasoning ability individuals.

The observed differences in cooperation for the first oneshot PD game are no longer significant for the first period of each repeated game. The high reasoning ability subjects, who cooperated significantly less at the beginning of the oneshot games, show no significantly lower cooperation levels at the beginning of the subsequent tasks (tests results are available upon request). High reasoning ability individuals seem to better anticipate the lower cooperation rate that will be attained in a series of one-shot games with different partners as opposed to a sequence of repeated interactions with the same partner.

# Individual Cooperation Dynamics

**Figure 6** shows individual cooperation percentages by task, period and treatment.

The percentage of cooperation decreases for all treatments as the one-shot PD game is repeated (task 1). However, the group with higher altruism and lower reasoning ability never reaches a 0% individual cooperation rate (the other treatment groups reach 0% individual cooperation in periods 5 to 9). Table SM2.10 in the Supplementary Material shows percentages of individual cooperation in the repetitions of the one-shot game, for all treatments.

Using a proportion test, in Table SM2.12 in the Supplementary Material we show that high reasoning ability participants (HR) cooperate significantly less in the one-shot PD game than low reasoning ability ones (LR) in the first two repetitions (column 1). Additionally, the percentage of cooperation is significantly higher for high altruism subjects (HA) than for low altruism ones (LA) for several periods, as can be seen in column 4.

As can be observed in **Figure 6**, in the RPD tasks individual cooperation not only is higher at the beginning but also sustained at around 40% to 60% until the very last period, when it falls abruptly (see details in Table SM2.11 in the Supplementary Material). However, last period individual cooperation rates are still positive, differently from task 1, for most treatments. No significant treatment effects appear in the RPD tasks, as we had already observed in our analysis of period one.

### Regression Analysis

In order to account for the effect of beliefs and of the stage game repetitions within each task together with the treatment, we run random-effects panel logit regressions. Results are reported in **Table 5**.

TABLE 5 | Random-effects panel logit regressions of individual cooperation on treatment, period and beliefs.

\*\*\*Coefficient significant at 1%, \*\*Significant at 5%. Standard errors in parentheses.

#### The variables used are the following:

In the regression for task 1 (the one-shot PD game) we consider "social belief " more appropriate than "individual belief " as a regressor, given that the individual is not always playing with a same partner.

The baseline treatment is "Low Altruism and Low Reasoning" (LALR). Within the "Low Altruism" subjects, the treatment with "High Reasoning" (LAHR) shows significantly lower cooperation in the one-shot PD game. On the opposite, a high level of altruism significantly increases the probability of cooperating for individuals characterized by "Low Reasoning" ability (HALR vs. the baseline LALR). The joint effect of high reasoning ability and high altruism appears to be null. In fact, there are no significant differences in cooperation between HAHR and LALR subjects, which could be due to the fact that the effects of a higher reasoning ability and a higher altruism go in opposite directions. This is coherent with the interaction effect we anticipated in Hypothesis 3.

We also observe that the higher the expectation on the percentage of players cooperating in that round, the higher individual cooperation. Moreover, each additional period significantly reduces the likelihood of cooperation. Gender has no significant effect.

Treatment effects disappear in the RPD tasks: none of the estimated coefficients for each of the three treatment dummies is significantly different from zero. In these tasks, thinking that the partner will cooperate significantly rises the probability of cooperation. There is a negative significant effect of period.

We can directly include reasoning ability and altruism measurements in these regressions rather than using a dummy for each group. Results are reported in **Table 6**. The variables used to measure reasoning ability and altruism are the following:

Although the correlation between reasoning ability and altruism was weak, we tested for collinearity in the estimated models. Results of these tests are reported in Table SM2.13 in the Supplementary Material. The Variance Inflation Factors are quite low (slightly above 1) for all regressors, indicating that there is no cause for concern.

For task 1 we obtain that reasoning ability has a significant negative effect while altruism increases the likelihood of cooperating, thus extending our Result 2 beyond the first period to all the one-shot PD games. The effect of the remaining variables is robust to the replacement of the treatment dummies by cognitive ability and altruism variables.

**Result 4:** In the one-shot PD games, the effect of reasoning ability on the likelihood of cooperation is negative while that of altruism is positive. Additionally, individual beliefs and period also significantly affect the cooperation decision. Gender is not relevant.

In task 2 reasoning ability continues to be significant for explaining cooperation. However, note that the direction of the effect is the opposite, that is, higher abstract reasoning leads to less cooperation in the one-shot PD and to more cooperation in RPD, thus confirming our Hypothesis 3. As we pointed out above, it seems that subjects with higher reasoning ability better recognize the different nature of the games played and the relatively lower opportunities of coordinating on cooperation that playing with a changing partner provides. Thus, these subjects seem to better adjust their behavior to the environment.

### **Result 5:** The effect of reasoning ability on cooperation is negative in the one-shot games but positive in the first RPD task.

In tasks 3 and 4 neither reasoning ability nor altruism affect cooperation. Instead, the belief that the partner will cooperate significantly increases the likelihood of cooperating in all tasks. In fact, this belief turns out to be highly correlated with past partner cooperation (which we have not included in the regression for this reason: Spearman's rho of 0.76, p < 0.001). Again, period has a significantly negative effect and gender plays no role.

**Result 6:** Experience with the RPD game takes over individual characteristics of the subjects in explaining their decision.

While reasoning ability significantly predicts cooperation behavior the first time the repeated game is played (task 2), individual characteristics do not seem to play a role when participants gain experience facing the RPD a second and a third time (tasks 3 and 4).

## Unconditional Cooperation

Using the information on beliefs, we computed the percentage of individuals who cooperate "unconditionally," that is, even if expecting defection, for each period of each task. The result is that very few individuals choose to cooperate thinking that the partner will defect. In the one-shot, on average only 1.5% of low altruism and 2.8% of high altruism participants' decisions are A/B. In the repeated tasks, on average <6% of both high and low altruism subjects' decisions are unconditionally cooperative. We interpret this result as evidence of very low unconditional cooperation. In fact, taking into account the payoff table of the game, we can observe that even a high altruism subject would find it hard to cooperate unconditionally. On average high altruism subjects were willing to sacrifice 4e out of 10e in the dictator game, while in the one-shot PD they should give up 10e and get nothing if they cooperate thinking that the partner is not going to cooperate. In fact no player gave up the whole 10e endowment in the DG.

**Result 7:** There is scarce evidence of unconditional cooperation, even for high altruism subjects.

# Paired Cooperation

By paired cooperation we refer to the situation where both members of a pair simultaneously decide to cooperate in a given period, thus obtaining the cooperative payoff of the Prisoners' Dilemma.

As can be seen in **Figure 7**, successful paired cooperation is obviously much lower in the one-shot than in the repeated PD. Only altruists show some positive cooperation at the beginning of task 1. The difference in paired cooperation between low and high altruism pairs is significant for the first one-shot game (z = −2.78 and p = 0.003). All treatments increase paired cooperation at the beginning of the RPD games, particularly high reasoning ability subjects which show steep and significant increases in the first two periods. Specifically, we find significant differences comparing the level of paired cooperation in period 2 vs. period 1 for high reasoning ability pairs (at 5% in tasks 2 and 3, marginally in task 4; test details in Table SM2.14 in the Supplementary

\*\*\*Coefficient significant at 1%, \*\*Significant at 5%. Standard errors in parentheses.

Material). There are no other treatment differences in reaching and sustaining high cooperation. Tasks 2 and 3 present levels of paired cooperation close to 40%, and task 4 reaches 60%.

**Result 8:** In the first one-shot game high altruism subjects exhibit higher levels of paired cooperation than low altruism ones.

**Result 9:** In the RPD game high reasoning ability subjects significantly increase paired cooperation in the first two periods, all treatments attaining and sustaining similarly high levels until one period before the last of each repetition, when cooperation crumbles.

# DISCUSSION

We study cooperative behavior in (PD) games using a neat 2 by 2 factorial design, considering high vs. low altruism and high vs. low reasoning ability. As in all the previous experiments with these games, we find evidence of cooperation in both oneshot and finitely repeated (PD). In particular, we confirm the result by Andreoni and Miller (1993) and Cooper et al. (1996) that a certain amount of cooperative play appears to be due to the altruistic nature of subjects. In fact, by using an external measure of altruism (giving in a Dictator's Game), we show that altruism positively affects the likelihood of cooperation in the one-shot PD games. Moreover, high altruism players seem to be more optimistic about their partners' behavior and they cooperate mainly thinking that their partner will also cooperate. Successful paired cooperation is very low in the one-shot games, with high altruism pairs being the only ones to reach positive levels.

As in the aforementioned studies and coherent with the "reputation building" hypothesis, we find that both individual and paired cooperation rates are much higher (40–60%) in the repeated PD games, and sustained for almost all periods, only to fall sharply in the last period of each task. Thanks to the elicitation of players' beliefs, we show that in our experiment cooperation is almost never unconditional: even altruistic subjects hardly cooperate if they think that their partner is going to defect. Altruism does not significantly increase neither individual nor paired cooperation in RPDs.

Interestingly, the effect of reasoning ability on individual cooperation changes sign depending on the type of PD game. Reconciling part of the previous literature and consistently with Burks et al. (2009)'s result for sequential PD, higher cognitive ability subjects appear to better adapt to the particular game played. In particular, they more accurately forecast their partner's behavior in the first repetitions of the one-shot games and at the beginning of the first RPD. Coherently, they tend to cooperate significantly less in the one-shot PD, as hinted in the lower continuation probability treatments of Proto et al. (2015). Also, they are more likely to cooperate in the first RPD, in line with what Jones (2008) found in his analysis using average intelligence scores. Differently to Al-Ubaydli et al. (2016), where paired cooperation is predicted by cognitive ability whereas individual cooperation is not, we do not find fundamental differences between individual and paired cooperation.

Reasoning ability is found to counteract the effect of altruism in the one-shot game. In fact, the joint effect of high reasoning ability and high altruism on the likelihood of cooperation appears to be no different from that of low reasoning ability and low altruism. However, while low reasoning ability individuals display similar behavior in both one-shot and RPD games, high reasoning ability subjects appear to better understand the nature of the one-shot (PD), changing then their decisions in the repeated version of the game.

Individual characteristics, however, fast reduce their weight in affecting subjects' decisions. While both reasoning ability and altruism explain individual cooperation in the one-shot PD and reasoning ability continues to be significant in the first RPD game, both characteristics become irrelevant as explicative variables when subjects gain experience in the RPD game. Instead, the variables affecting individual cooperation are period and subject beliefs. The latter could still be mediated by subject type, but in a more dynamic and adaptive way, as beliefs in the RPD are highly correlated with past partner cooperation. With experience in the RPD, reached and sustained cooperation end up being similar among all groups. Thus, in a (PD) setting, altruism and reasoning ability significantly affect behavior in a situation in which no future consequence of choices is expected. This effect appears to be diluted when building a reputation can be used to reach higher payoffs. Indeed, transforming a social relationship into repeated interactions appears to be key to achieve mutual cooperation (Axelrod, 1984).

As future research, personality traits could also be added as determinants of cooperation, such as agreeableness or extraversion, as in Pothos et al. (2011), Proto et al. (2015), or Kagel and McGee (2014). They could be added as controls rather than as treatment variables, because the latter option would much complicate the treatment structure and impose high demands on the number of participants. An efficient alternative would be to program algorithmic players with a selection of frequently studied strategies and make them interact with human players, as in Hilbe et al. (2014). Also, having an increased age and culture variability could add insights on the determinants of cooperation.

# ETHICS STATEMENTS

This study was carried out in accordance with the recommendations of the ethical committee from the Universitat Jaume I. Participants gave informed consent in accordance with the Declaration of Helsinki. All participants in the subject database from the LEE at Universitat Jaume I in Castellón have voluntarily signed to participate in economic experiments and can freely decide whether they want to take part or not in each proposed experiment. No deception takes place in any experiment run at the LEE. No vulnerable populations were involved in the study.

# AUTHOR CONTRIBUTIONS

All authors collaborated in the development of the idea, the design of the project and the running of the sessions. IB programmed the software. AJ and IB developed the database and carried out most of the analyses. MP, IB, and GS wrote the article. All authors revised and accepted the written version.

# FUNDING

Financial support by Universitat Jaume I (project P1.1B2015- 48) and the Spanish Ministry of Economics and Competitiveness (projects ECO2013-44409-P and ECO2015-68469-R) is gratefully acknowledged.

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg. 2017.00596/full#supplementary-material

# REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Barreda-Tarrazona, Jaramillo-Gutiérrez, Pavan and Sabater-Grande. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# At Least I Tried: The Relationship between Regulatory Focus and Regret Following Action vs. Inaction

Adi Itzkin, Dina Van Dijk and Ofer H. Azar\*

Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

Regret is an unpleasant feeling that may arise following decisions that ended poorly, and may affect the decision-maker's well-being and future decision making. Some studies show that a decision to act leads to greater regret than a decision not to act when both resulted in failure, because the latter is usually the norm. In some cases, when the norm is to act, this pattern is reversed. We suggest that the decision maker's regulatory focus, affects regret after action or inaction. Specifically, promotion-focused individuals, who tend to be more proactive, view action as more normal than prevention-focused individuals, and therefore experience regulatory fit when an action decision is made. Hence, we hypothesized that promotion-focused individuals will feel less regret than prevention-focused individuals when a decision to act ended poorly. In addition, we hypothesized that a trigger for change implied in the situation, decreases the level of regret following action. We tested our hypotheses on a sample of 330 participants enrolled in an online survey. The participants received six decision scenarios, in which they were asked to evaluate the level of regret following action and inaction. Individual regulatory focus was measured by two different scales.  Regret following inaction was not affected by regulatory focus. In addition, a trigger for change decreases regret following action. Orthodox people tend to attribute more regret than non-orthodox to a person who made an action decision. The results contribute to the literature by showing that not only the situation but also the decision maker's orientation affects the regret after action vs. inaction.

#### Edited by:

Nikolaos Georgantzis, University of Reading, UK

#### Reviewed by:

Pablo Brañas-Garza, Middlesex University, UK Giuseppe Attanasi, University of Strasbourg, France

> \*Correspondence: Ofer H. Azar<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology

> Received: 18 June 2016 Accepted: 13 October 2016 Published: 27 October 2016

#### Citation:

Itzkin A, Van Dijk D and Azar OH (2016) At Least I Tried: The Relationship between Regulatory Focus and Regret Following Action vs. Inaction. Front. Psychol. 7:1684. doi: 10.3389/fpsyg.2016.01684 Keywords: regulatory focus, regulatory fit, promotion focus, prevention focus, regret, action, inaction

# INTRODUCTION

Every decision that we make in our life carries the risk that we might regret it. But what type of decisions will be regretted more: decisions of doing something or decisions of not doing anything? In the current paper we suggest that individual differences in regulatory focus would affect individuals' tendency to regret more what they did or what they did not do.

Regret is an unpleasant feeling that is aroused after retrospection that involves awareness to the negative aspects of a decision. The regret process involves running a mental re-creation of what actually happened vs. what could have happened, comparing these two options and deciding that the decision process and the outcome were suboptimal (Zeelenberg et al., 2002; Roese et al., 2009; Das and Joffe, 2012). The level of regret depends on an individual's perception of the mental gap between what happened as opposed to what could have happened. The greater the gap, the stronger the regret (Das and Kerr, 2010). Regret could lead to regret aversion, which further encourages people to learn from past decisions in order to avoid similar experiences in the future (Zeelenberg and Beattie, 1997; Zeelenberg et al., 2002; Roese et al., 2009; Das and Kerr, 2010; Das and Joffe, 2012).

There are contradictory findings regarding what induces more regret: action or inaction. Action is considered as doing something that changes the current situation, such as deciding to go out for dinner or changing a strategy when trying to solve a problem. Inaction, on the other hand, is considered as doing nothing or keeping the status quo, such as staying home or keeping the same strategy already used. Early research found that because inaction is usually the norm, action, which violates the norm, leads to greater regret (Kahneman and Tversky, 1982; Kahneman and Miller, 1986). This finding has been later replicated in numerous studies (e.g., Landman, 1987; Gleicher et al., 1990; Baron and Ritov, 1994; Gilovich and Medvec, 1995; Miller and Taylor, 1995; Ritov and Baron, 1995; N'gbala and Branscombe, 1997; Van der Pligt et al., 1998; Ordóñez and Connolly, 2000).

Yet, other studies demonstrated that under certain conditions inaction can produce more regret than action (Gilovich and Medvec, 1995; Zeelenberg et al., 2002). For example, Gilovich and Medvec (1995) indicated that regret perception depends on the time horizon. Specifically, action is more regrettable in the short term, while inaction is more regrettable in the long term. Following this view, Zeelenberg et al. (2002) broadened Kahneman and Tversky (1982) theory and have added the inaction effect, which becomes relevant when action is perceived as desirable and needed, whereas inaction is perceived as less desired. Zeelenberg et al. suggested that what is considered normal can be influenced by a relevant past decision. In particular, when prior outcomes are positive or absent, inaction is considered more normal and people will attribute more regret to action than to inaction. However, when prior outcomes are negative, action becomes the more normal decision and more regret will be attributed to inaction.

Bar-Eli et al. (2007) showed that sometimes action can be the norm even without prior outcomes. They found that soccer goalkeepers in penalty kicks perceive action (jumping to one of the sides) as more normal than inaction (staying at the center of the goal) and consequently failed inaction produces more regret than failed action. Consequently, goalkeepers almost always choose action even though this actually reduces their chances to stop the ball. Azar (2013) examined the impact of previous outcomes not on regret but on decisions in a business strategy context. He found that whether a strategy was previously successful or not did not affect the likelihood that it will be continued or changed, in scenarios where the previous outcome was not informative about the future, but could trigger emotional reaction, such as regret.

Other factors that affect the relationship between failure associated with action vs. inaction and level of regret are desirability and consistency (Seta et al., 2001). Seta et al. suggested that errors associated with action or inaction are less desirable and produce more regret when they are inconsistent with the decision maker's orientation (action or inaction) than when they are consistent. The effect of consistency and desirability was further demonstrated by McElroy and Dowd (2007). Building upon Seta et al. (2001) and McElroy and Dowd (2007) we suggest in the current paper that the level of regret following action or inaction is determined by the individual's regulatory focus, through the mechanism of regulatory fit. We rely on a wellestablished comprehensive motivational theory of regulatory focus (Higgins, 1997, 1998) to explain mixed evidences regarding regret following decisions of action vs. inaction.

Regulatory Focus Theory (Higgins, 1997, 1998), proposes that human motivation consists of two regulatory foci: promotion and prevention focus. People with promotion focus are motivated to achieve accomplishments, aspirations, and ideals, they are sensitive to gain - non-gain situations and to the presence or absence of positive outcomes. In contrast, individuals with prevention focus are motivated to attain security, responsibility, and duties. They are sensitive to loss - non-loss situations and to the presence or absence of negative outcomes (Higgins and Tykocinski, 1992; Friedman and Förster, 2001; Cesario et al., 2004; Förster et al., 2004). Regulatory focus can emerge as a chronic characteristic (personal disposition) as well as a situational (context-induced) variable (Higgins, 1997, 1998).

The two motivational foci are related to different types of strategies that are used to achieve individuals' goals. As was shown in numerous studies (Crowe and Higgins, 1997; Higgins, 1997; Shah et al., 1998; Liberman et al., 1999; Freitas and Higgins, 2002; Chernev, 2004; Avnet and Higgins, 2006), promotion focus individuals use approach and eagerness means to pursue their goals, tend to make changes and to take risks, try to achieve gains, and are prone to action; whereas prevention focus individuals use avoidance and vigilance means to pursue their goals, tend to maintain stability and to keep the status quo, try to avoid losses, and tend to caution and inaction. The inaction preference can be so profound that prevention-focused individuals might choose the status quo even if it is not the profitable one (Chernev, 2004).

According to regulatory focus theory, when the individuals' regulatory focus matches their goal pursuit means, they experience regulatory fit, which subsequently enhances their belief in what they are doing and the significance of their decisions (Higgins, 2000, 2005, 2006). Promotion focus fits goal pursuit means, such as eagerness and approach strategies (e.g., taking risks), whereas prevention focus fits goal pursuit means, such as vigilance and avoidance strategies (e.g., avoid risks). Under regulatory fit, people will judge a decision they made as more "right," value it more, and feel more engaged to their decision, than under non-fit condition (Camacho et al., 2003; Higgins, 2005). For example, Camacho et al. (2003) asked subjects to imagine themselves having a conflict with another person and to evaluate the other person's strategy of resolving the conflict. Promotion-focused subjects evaluated eager strategies (e.g., encouraging you to succeed) as more right than preventionfocused subjects, whereas prevention-focused subjects evaluated vigilant strategies (e.g., removing anything that might cause trouble) as more right than promotion-focused subjects. Based on the regulatory fit principle, it can be argued that in situations of regulatory fit, because people feel more right about what they are doing, there will be less regret.

Although regret is a central emotion, the influence of regulatory fit on regret is still in the first stages of investigation. Kwak and Park (2008) showed that fear from anticipated regret increases the "sunk cost" effect (continue investing in a hopeless situation) under regulatory fit condition. However, our research focuses on the influence of regulatory fit on experienced regret in post-choice evaluation. Although conceptual models have suggested including regulatory fit as an integral part of the regret process, little has been conducted in this research area (Roese et al., 2007; Das and Kerr, 2010). We have found only one study that examined the effect of regulatory focus on regret (Church and Iyer, 2012), however, this study was not designed to test the different level of regret following action vs. inaction. The authors found (in contrast to their prediction) that people with higher promotion focus tended to regret their actions more than people higher on prevention focus. However, this was not compared to the level of regret following inaction. Thus, we cannot infer from these findings about our research questions.

In the present study we hypothesize that regulatory fit leads to less regret than regulatory non-fit. Specifically, a decision of action will fit individuals in promotion focus, and a decision of inaction will fit individuals in prevention focus. In other words, we argue that inaction will be considered as more normal behavior under prevention focus than under promotion focus, whereas action decisions will be viewed as more normal under promotion focus. Consequently, in line with norm theory (Kahneman and Miller, 1986), we hypothesize that the phenomenon of attaching more regret to action that fails than to inaction that fails, will be lower under promotion focus and higher under prevention focus. Thus, our hypotheses are:

**Hypothesis 1**: When two decisions resulted in failure, one is an action decision and another is an inaction decision, individuals in promotion focus will be more likely to attribute the lower regret to the action decision than individuals in prevention focus.

**Hypothesis 2**: Individuals in promotion focus will attribute **less** regret than individuals in prevention focus to an **action** decision that resulted in failure.

**Hypothesis 3**: Individuals in promotion focus will attribute **more** regret than individuals in prevention focus to an **inaction** decision that resulted in failure.

Hypothesis 1 relates to a binary question of regret (i.e., "Who feels more regret, the person who chose action or the person who chose inaction?"). Due to the binary nature of the question, this hypothesis is essentially identical to the symmetric hypothesis ("When two decisions resulted in failure, one is an action decision and another is an inaction decision, individuals in prevention focus will be more likely to attribute the lower regret to the inaction decision than individuals in promotion focus") and therefore the results, which are presented according to Hypothesis 1, can also be interpreted as addressing this symmetric hypothesis.

Hypotheses 2 and 3 relate to two continuous variables of regret (i.e., level of regret following action and level of regret following inaction). Additionally, since regulatory focus is an individual tendency but could also be temporarily induced, all hypotheses will be tested by both individual and induced regulatory focus.

Along the lines of Zeelenberg et al. (2002), we hypothesize that a negative prior outcome makes action (a change in the status quo) more normal than absent such a negative prior outcome. In other words, a negative prior outcome creates a trigger for a change, and such a trigger increases the normality of choosing action and reduces the normality of choosing inaction, leading to reduced regret from failed action and increased regret from failed inaction. Moreover, we believe that not only a prior negative outcome but also a change in the environment may create a trigger for change, increasing the normality of action and reducing the normality of inaction, and therefore reducing the regret from action decisions compared to inaction decisions. For example, a trigger for change could stem from changing the targeted production level in one's work environment, which implies that a change in one's work strategy might be needed. To sum, we hypothesize:

**Hypothesis 4**: A trigger for change (caused by prior negative outcome or by a change in the environment) will **reduce** regret after failed **action** and **increase** regret after failed **inaction**.

Several demographic variables might affect the tendency to regret action or inaction. People's level of religiosity may affect their tendency to favor action vs. inaction in different life situations. For example the Orthodox in Israel are known for their aversion of changes and by their clinging to the status quo, holding the motto of "to any proposal for change say 'no"' (Lehmann and Siebzehner, 2009). Therefore, they might show more regret following action. Other demographic variables, such as gender, age, and income level could also affect the level of regret and therefore were taken into account in our analysis.

# METHODS

To test our hypotheses, we conducted an online experiment that included six scenarios in which regulatory focus (prevention vs. promotion) was induced, and regret level was measured after scenarios of failed action decisions vs. failed inaction decisions. In addition, chronic individual regulatory focus as well as demographic and personal variables were measured. The study received an approval from the Human Subjects Research Committee of the University.

# Sample

A total of 330 Israeli subjects were recruited voluntarily through a polling service company and were paid in exchange for their participation. One hundred and seventy three (52.4%) were female, age range was between 25 to 60 years old, and the mean age was M = 39 (SD = 10.14). Income level ranged between much below average (n = 41; 12.4%), below average (n = 69; 20.9%), average (n = 128; 38.7%), above average (n = 66; 20%), and much above average (n = 21; 6.3%), with 5 missing values. In terms of religiosity level, 171 (51.8%) were secular, 76 (23%) traditionalists, 45 (13.6%) orthodox, and 38 (11.5%) ultra-orthodox.

# Procedure

The study consisted of three parts. In the first part participants filled a consent form and then filled the chronic regulatory focus measure. In the second part the subjects were divided randomly into three treatment groups: Induced promotion focus (n = 94), induced prevention focus (n = 116), and a control group (n = 120). The manipulation included a word-completion task, in which subjects were asked to complete missing words in a text, using specific words that were provided in a list. The induced promotion focus manipulation used a list of promotion words (e.g., gain, aspirations, success), whereas the induced prevention focus manipulation used a list of prevention words (e.g., loss, obligations, failure). In the control condition no task was given. In order to check the manipulation the participants were asked to rate 8 behavior tendencies related to either a promotion focus (e.g., eager) or a prevention focus (e.g., vigilant), on a 10-point scale.

The third part of the experiment involved six scenarios. Each scenario presents an uncertain situation with two possible decisions: to retain the status quo (inaction) or to make a change (action). One decision maker in the scenario chooses action and the other chooses inaction, and both fail. The first two scenarios replicated those used in previous studies (Kahneman and Tversky, 1982; Zeelenberg et al., 2002), and the sixth scenario is somewhat similar to that in Gilovich and Medvec (1995). The additional new scenarios were developed to examine the robustness of the results to different contexts and situations, keeping the same structure of failed action vs. inaction. Three of the scenarios (2, 5, 6) contained a trigger for change, either a prior negative outcome or a change in the environment, while the other three scenarios did not contain any signal for the need of change (1, 3, 4). The scenarios were always presented in the same order, from 1 to 6.

After each scenario the subjects were asked to indicate who feels more regret (the one who acted or the one who did not act). This was the question used in Kahneman and Tversky (1982) and in Zeelenberg et al. (2002). Thus, in order to precisely replicate the original studies, we did not add any other questions that may affect the answers to the original question. In the four additional scenarios, however, we added two questions that asked the subjects to estimate the regret level of each decision maker on a 0–100 scale. We assumed that when participants are asked to estimate the regret level of a person in a hypothetical scenario they will use their own experience and personality to make their estimation. The six scenarios appear in the Appendix.

# Measures

Individual Regulatory Focus was measured with two measures: one is the scale of Lockwood et al. (2002), which is the most common scale for measuring regulatory focus, and the other is the Outcome-Based Measure (OBM; Schödl et al., 2013), a recently developed scale for regulatory focus.

Demographic variables age, gender, income, and religiosity level, were provided by the polling service company. Income was measured on an ordinal five-point scale (much below average, below average, average, above average, much above average). Religiosity level contained four categories that represent four main Israeli sectors: secular, traditionalist, orthodox, and ultraorthodox. We recoded religiosity into a dichotomous variable with secular and traditionalist coded as "0" (non-orthodox), and orthodox and ultra-orthodox coded as "1."

# RESULTS

# Induced Regulatory Focus Manipulation

We first conducted a manipulation check for the regulatory focus manipulation. A set of eight Independent-Sample ttests showed no differences between promotion and prevention conditions in terms of the behavior tendencies evoked by the word-completion task (t-tests ranged between −0.11 < t < 1.61; and significance levels.12 < p < 0.91). None of the eight behavior tendencies revealed significant difference between the two regulatory focus manipulations. As a result, the induced regulatory focus was not used in further analyses, but in order to control the potential effect of the manipulation, we added to the regressions two dummy variables for the promotion and prevention treatments (denoted by Promotion\_Tr and Prevention\_Tr in the regressions), where the control treatment with no word-completion task is the benchmark. Thus, further analyses tested hypotheses 1–3 only with regards to the individual measures of regulatory focus and not regarding the induced regulatory focus.

# Who Feels More Regret?

Next, we considered the first question in each scenario, asking which of the two decision makers feel more regret, the person who acted or the person who did not act (the dichotomous measure of regret). For each subject we only have a binary response about who felt more regret, but aggregating over all the subjects we can get the proportion of subjects that attributed more regret to action or to inaction. **Table 1** presents these proportions and the test of whether the underlying probability is different from 0.5 (using the binomial distribution).

The results show that in scenario 5 the proportions are exactly 50–50% and in scenario 2 more people attribute greater regret to inaction (55.2 vs. 44.8%), but the difference is not statistically significant. In the other four scenarios a higher regret was attributed more often to the person who acted than to the person who did not act, and the difference is statistically significant at the 5%-level (using a 2-tailed binomial test). **Table 1** also shows that in three scenarios (2, 5, and 6) there were less than 57% who attributed more regret to action (than inaction). In the other three scenarios (1, 3, 4) more than 68% attributed more regret to action. The difference between these two groups of scenarios will be discussed later.

To test how regulatory focus is related to regret following action vs. inaction, we conducted two sets of six logistic regressions (one for each scenario) on the dichotomous measure of regret, namely, which person regret more, the one who acted (coded 1) or the one who did not act (coded 0). The first set of regressions included the following predictors: Demographics (age, gender, religiosity, and income), two dummy variables of the manipulation treatment of regulatory focus, and the individual regulatory focus measure of Lockwood. The second set of regressions was similar but used the OBM scale instead of Lockwood as the measure of individual regulatory focus. The results of the 12 logistic regressions, summarized in **Table 2**, show some support for Hypothesis 1. Specifically, **Table 2** demonstrates that in scenarios 2 and 5 (with both measures of regulatory focus), and scenario 6 (only with the OBM scale), individual regulatory focus had a significant effect in the predicted direction, namely, the higher the promotion focus the lesser the probability of attributing more regret to the person who acted (compared with the one who did not act).

To be able to analyze the six scenarios together and derive more general conclusions, we created a database that aggregates the scenarios but records the unique subject ID in each observation. We then ran regressions on the combined data of scenarios 1–6 (clustered by subject ID), which are reported at the bottom of **Table 2**. These two regressions revealed that individual regulatory focus was statistically significant in the predicted direction (p = 0.017 using Lockwood's scale, p = 0.001 using the OBM scale). That is, the higher the promotion level, the less likely is the subject to attribute greater regret to the action decision (vs. inaction).

To sum, the effect of regulatory focus on the likelihood of attributing lower regret to the action decision (compared with the inaction decision) was obtained in three out of six scenarios when they are considered separately, and in the total measure of regret across all six scenarios. In addition, except for scenario 6, these effects were consistent across two different measures of regulatory focus. Thus, our results partially support hypothesis 1.

In addition to the effect of regulatory focus, subjects' religiosity level also had a significant effect on attributed regret. Specifically, in scenarios 2 and 5 and in the aggregated scenarios 1–6 (see **Table 2**) religiosity was positively and significantly related to the probability of attributing more regret to the person who acted. In scenarios 1 and 3 the effect of religiosity was positive and marginally significant (p-levels ranged between 0.06 and 0.07). All these mentioned effects of religiosity were consistently found across the two sets of regressions with both scales of regulatory focus. The positive effect of religiosity indicates that orthodox people are more likely than non-orthodox people to attribute more regret to action (compared to inaction).

# Regret Levels Following Action vs. Inaction

In order to test Hypothesis 2 we conducted two sets of linear regressions on the continuous measure of regret following action, which was measured in scenarios 3–6. Two sets of four linear regressions (for each of the four scenarios 3–6) were conducted on the level of regret attributed to the person who acted in the

TABLE 1 | Who feels more regret–the person choosing action or inaction?

The right column presents the 2-tailed p-value of the test (using the binomial distribution) of whether the probability of a subject attributing more regret to action (or inaction) is different from 0.5.

#### TABLE 2 | Logistic regressions: does action produce more regret than inaction?

(Continued)

#### TABLE 2 | Continued

The dependent variable is ActMoreRegret, a dummy variable that equals one if the subject thinks that the person who acted feels more regret than the one who did not act. The table reports the robust standard errors. The last regressions, on the combined data of scenarios 1–6, are clustered by subject ID. Significant effects are bold.

scenario. The predictors in the logistic regressions were used also here. In addition, the level of regret attributed to the person who did not act (by the same subject in the same scenario) was also included as an independent variable, in order to control for regret following inaction when predicting regret following action.

**Table 3** presents the regression results that show what affects the regret attributed to the action decision. Because the question about the level of regret after the action decision was introduced only in scenarios 3–6, the results do not include scenarios 1–2. In scenarios 4, 5 (with both measures of regulatory focus), and 6 (only with the OBM scale), individual regulatory focus was significant at the 5% level in the predicted direction, namely, the higher the promotion focus, the lower the regret level attributed to the action decision. This effect was obtained beyond the positive effect of the level of regret attributed to the inaction decision. In other words, despite the fact that the level of regret attributed to inaction was positively and significantly related to the level of regret attributed to the action decision, the unique effect of regulatory focus on regret attributed to action was significant, supporting Hypothesis 2. To get an overview of the general findings across all scenarios, we also ran two regressions on the combined data of scenarios 3–6 (clustered by subject ID), reported at the bottom of **Table 3**. In line with Hypothesis 2, individual regulatory focus measured by both Lockwood's scale and the OBM scale was significant, such that the higher the promotion focus, the lower the regret attributed to the action decision.

To sum, the effect of regulatory focus on the attribution of regret to an action decision was obtained in three out of four scenarios and in the total measure of regret across all four scenarios. In addition, these effects were consistent across two different measures of regulatory focus. Thus, our results support hypothesis 2.

In order to test Hypothesis 3, we ran two additional sets of regressions on the level of regret following inaction. The same predictors were used as in the previous regressions, but this time we controlled for the regret level following action, since we predicted the level of regret following inaction. The results of these linear regressions are shown in **Table 4** and surprisingly do not support Hypothesis 3. Specifically, no effect of regulatory focus on the level of regret following inaction was revealed (except for one effect of the Lockwood's scale in scenario 6). As can be seen in **Table 4** the regret level following action positively predicts the level of regret following inaction, but regulatory focus has no unique effect on regret following inaction. Thus, the results did not confirm Hypothesis 3.

# The Effect of a Trigger for Change

We now turn to examine Hypothesis 4, according to which a trigger for change lowers the level of regret attributed to action. Scenarios 2, 5, and 6, included a trigger for change, whereas scenarios 1, 3, and 4 did not include any trigger for change. Scenario 2, which replicates a study of Zeelenberg et al. (2002), includes a negative prior outcome (losing the prior game), after which the coach has to decide whether to change the team. The prior loss creates a trigger to do something different, i.e., a trigger for change. Similarly, in Scenario 6, which deals with a decision of students to change or not their university, it is mentioned that the students are unhappy with their university, again creating a trigger for change. In scenario 5, which deals with two employees who have weekly manufacturing targets, it is mentioned that this week the target was higher than usual. This is not a prior negative outcome but it is an important change in the environment, which can be a trigger for change in the decision (which machine parameters to adopt). In contrast to those three scenarios, scenarios 1, 3, and 4, describe a decision of two people to change or not to change, without any additional information that could be a trigger for change. For example, scenario 1 (a replication of a scenario from Kahneman and Tversky, 1982) describes two people who decide to change/not change a stock, but no reason or additional information regarding the necessity of a change is given. Similarly, scenarios 3 and 4 present two decisions to change/not change a project (scenario 3), or a supplier (scenario 4), but no additional information is given for a prior negative outcome of the current project or supplier, or a significant change in the environment. Therefore, no apparent trigger for change is created in scenarios 1, 3, and 4.

#### TABLE 3 | Linear regressions explaining regret following action.

The dependent variable is the regret (on a 0–100 scale) following a failed action decision. The table reports the robust standard errors. The last regressions, on the combined data of scenarios 3–6, are random-effects GLS regressions clustered by subject ID. Significant effects are bold.

#### TABLE 4 | Linear regressions explaining regret following inaction.

The dependent variable is the regret (on a 0–100 scale) following a failed inaction decision. The table reports the robust standard errors. The last regressions, on the combined data of scenarios 3–6, are random-effects GLS regressions clustered by subject ID. Significant effects are bold.

**Table 1** already demonstrates that something is different between the trigger for change (TFC) scenarios 2, 5, and 6, and the no-TFC scenarios 1, 3, and 4. In particular, the proportion of subjects who attribute greater regret to action than to inaction ranged between 68.7 and 74.5% in the no-TFC scenarios, but only 44.8–56.5% in the TFC scenarios. Considering the continuous variables of regret levels following action and inaction, we see again a remarkable difference between the TFC scenarios (now only scenarios 5 and 6 because no continuous regret levels were elicited for scenarios 1 and 2) and the no-TFC scenarios 3 and 4. More specifically, in the no-TFC scenarios (3 and 4), the regret from action was higher than regret from inaction and the difference was statistically significant (68.31 vs. 56.29, p = 0.0000 in Scenario 3; 69.33 vs. 55.94, p = 0.0000 in Scenario 4). However, in the TFC scenarios (5 and 6) the regret levels from action and inaction were very close and not statistically significant (61.38 vs. 58.35, p = 0.1027 in Scenario 5; 59.27 vs. 57.79, p = 0.4274 in Scenario 6). Overall, the level of regret after action was significantly higher when no trigger for change exists compared to the TFC scenarios (68.82 vs. 60.33, p = 0.0000). However, the regret from inaction was similar regardless of a trigger for change (56.11 vs. 58.07, p = 0.1747).

Hypothesis 4 was tested on the combined data of scenarios 1–6 (clustered by subject ID). We ran logistic regressions on the dichotomous measure of regret with the same independent variables as in the previous logistic regressions, but also adding a dummy variable for the trigger for change (coded "0" for no-TFC scenarios, and "1" for TFC scenarios). In addition, in order to test whether the effect of regulatory focus differs between TFC and no-TFC scenarios, we added the interaction between the trigger for change and the individual regulatory focus (TFC X promotion focus). **Table 5** summarizes the results of the two regressions (one with Lockwood's promotion focus and one with the OBM promotion focus).

As can be seen in **Table 5**, according to our prediction, the trigger for change had a significant negative effect on the probability of attributing more regret to action, meaning that when there is a trigger for change, less regret is attributed to action (compared to no trigger for change). This finding was consistent across the two measures of individual regulatory focus and further confirmed Hypothesis 4. In addition, while the main effect of regulatory focus was non-significant, the interaction between regulatory focus and the trigger for change was significant and negative. This significant interaction together with the lack of significant effect of the promotion focus variable itself, suggests that when asking subjects the binary question of who feels more regret, there is no significant effect of promotion focus in scenarios without a trigger for change, but there is a significant effect of promotion focus once a trigger for change is introduced. In particular, a trigger for change makes it less likely that the greater regret will be attributed to the person who chose action. These findings were consistent across the two measures of individual regulatory focus.

In addition, subjects' religiosity level also had a significant effect on attributed regret, indicating that orthodox people are more likely than non-orthodox people to attribute more regret to action (p = 0.000 for both measures of regulatory focus). This effect was consistent with the effects of religiosity that were found in the previous logistic regressions (see **Table 2**).

We also tested Hypothesis 4 on the two continuous measures of regret: regret following action (see **Table 6**) and regret following inaction (see **Table 7**). Two sets of linear regression models were conducted on the combined data of scenarios 3– 6 (clustered by subject ID). The independent variables were the same as in the previous regression, except that we controlled for regret following inaction when predicting regret following action; and we controlled regret following action when predicting regret following inaction.

As can be seen in **Table 6**, the trigger for change had a significant negative effect on regret following action, meaning that when there is a trigger for change, less regret is attributed to action. This finding further confirms Hypothesis 4. In addition,

The dependent variable is ActMoreRegret, a dummy variable that equals one if the subject thinks that the person who acted feels more regret than the one who did not act. TFC, Trigger for Change. The table reports the robust standard errors. The regressions are clustered by subject ID. Significant effects are bold.

The dependent variable is the regret (on a 0–100 scale) following a failed action decision. TFC, Trigger for Change. The table reports the robust standard errors. The regressions are random-effects GLS regressions clustered by subject ID.

Significant effects are bold.

TABLE 7 | Linear regressions explaining regret following inaction: adding the trigger for change.

The dependent variable is the regret (on a 0–100 scale) following a failed inaction decision. TFC, Trigger for Change. The table reports the robust standard errors. The regressions are random-effects GLS regressions clustered by subject ID.

Significant effects are bold.

the effect of regulatory focus was significant such that the higher the promotion focus, the lower the regret following action (supporting Hypothesis 2 as in our earlier findings). The interaction between trigger for change and regulatory focus was non-significant. This pattern of results was consistent in both measures of individual regulatory focus.

Finally, as can be seen in **Table 7** and in line with Hypothesis 4, the trigger for change had a positive effect on regret following inaction, meaning that when there is a signal that a change might be needed, there is more regret following inaction. However, this effect was weaker than the effect of TFC on regret from action (the coefficients of TFC on regret from action are −7.8 and −8.3 in the two regressions, compared to coefficients of +3.6 and +2.1 on regret from inaction). In addition, this effect was statistically significant for the regret from inaction only when the Lockwood's scale was used. When using the OBM scale this effect was not statistically significant, although it had a positive coefficient as predicted. The individual regulatory focus had no effect on regret following inaction, similar to the results in **Table 4**, and once again not consistent with Hypothesis 3. The interaction between individual regulatory focus and the trigger for change also had no effect on regret from inaction.

In sum, the data strongly support our prediction that the existence of a trigger for change decreases the level of regret following action, but only partially support our prediction that it increases the level of regret following inaction. In addition, the effect of regulatory focus was similar to our earlier findings, namely, promotion focus decreases regret following action (supporting Hypothesis 2), but does not increase regret following inaction (not supporting Hypothesis 3).

# Summary of Results

Our results provide partial support for hypotheses 1, 2, and 4, but did not support hypothesis 3. When testing whether more regret is attributed to action decision or to inaction decision, we found that regulatory focus was significantly related to regret in three out of six scenarios (2, 5, and 6) and when the effect is calculated across all six scenarios. The direction of the effect indicates that the higher the promotion focus, the lower the probability of attributing more regret to action. Similarly, when testing the regret following action (where it was measured on a 0–100 scale, i.e., in scenarios 3–6), the same effect of regulatory focus was found. Specifically, regulatory focus was related to regret in three out of four scenarios (4, 5, and 6) and when the effect is calculated across all four scenarios, such that the higher the promotion focus, the lower the attributed regret following action. However, when testing regret following inaction, there was no effect of regulatory focus in any of the scenarios (except for scenario 6 in Lockwood's scale), and also not when calculating the total effect across all four scenarios. In addition, according to our prediction, we found that when the situation contains a trigger for change, less regret is attributed to action and more regret is attributed to inaction (although the effect of TFC on inaction was not always statistically significant and it was weaker than its effect on action). Finally, relatively high consistency was found in the results pattern between the two scales of regulatory focus. This consistency further strengthens the robustness of our findings.

# DISCUSSION AND CONCLUSIONS

The present study examines the effect of regulatory focus on regret feelings following action vs. inaction decisions. The results indicate that individual differences in regulatory focus are related to the level of regret that emerges after making a decision that results in failure, and in particular, after making an action decision. The mechanism that explains the effect of regulatory focus on regret stems from the principle of regulatory fit. According to this principle, when the individual regulatory focus of decision makers fits their goal pursuit means or strategies, they feel more right about what they are doing (Higgins, 2000, 2005, 2006). Because an action decision fits more promotion focus, whereas an inaction decision fits more prevention focus (e.g., Chernev, 2004), we predicted that action will be less regrettable for promotion-focused individuals, whereas inaction will be less regrettable for prevention-focused individuals. Our results indeed show that promotion-focused individuals attribute less regret to action decisions than prevention-focused individuals. However, no difference was found between individuals with promotion and prevention foci with regard to regret from inaction decisions.

# Regulatory Focus and Regret

Our findings contribute to the regulatory focus research arena by expanding the role of individual regulatory focus to the domain of regret. So far numerous studies have found that regulatory focus affects people's decisions and choices (e.g., Aaker and Lee, 2001; Chernev, 2004; Lee and Aaker, 2004; Avnet and Higgins, 2006), strategies (e.g., Crowe and Higgins, 1997; Lockwood et al., 2002) and emotions (e.g., Higgins et al., 1997). However, as far as we know no study has investigated the effect of regulatory focus on post-choice regret. While previous research showed that people valued more decisions that were made under conditions of regulatory fit, than under non-fit (e.g., Higgins et al., 2003), the current research extends previous research by showing that under regulatory fit condition, people are also less likely to regret their decisions. Specifically, since action decision fits promotion focus orientation, an action decision is regretted less by promotionfocused individuals than by prevention-focused individuals. Understanding the impact of regulatory focus on regret from action vs. inaction could have implications for individuals' wellbeing and emotional regulation. For example, we can predict that prevention-focused individuals will be more sensitive to the negative effects of regret emerged by action decisions that failed; such negative effects could be reduced well-being, guilt or other negative feelings. On the other hand, our results do not suggest that the opposite effect is true for promotionfocused individuals, namely, inaction decisions that failed do not seem to harm promotion-focused individuals (compared to prevention ones). Thus, we suggest that prevention-focused individuals will be more sensitive to the harmful effect of regret following action decision, while promotion-focused individuals will be more resilient to such harmful effect. This notion is consistent with previous research suggesting that preventionfocused individuals might be more vulnerable to reduced wellbeing, whereas promotion focus is related to more resiliency (Van Dijk et al., 2013). Future studies are encouraged to further investigate the effect of regulatory focus on regret and regret consequences, such as reduced well-being, negative feelings and regret aversion.

# Action and Inaction Asymmetry

Our findings show asymmetry in the effect of regulatory focus on regret following action vs. inaction. This asymmetry has not been revealed by previous studies. When using a binary measure of regret (i.e., who regrets more: a person who acted or a person who did not act), we found that promotion focus decreased the probability of attributing more regret to action than to inaction. However, using the binary question we still do not know whether this effect results from promotion-focused individuals attributing less regret to action, more regret to inaction, or both. The use of additional two continuous measures of regret (i.e., regret following action and regret following inaction) revealed an asymmetric pattern between action and inaction. Specifically, promotion-focused individuals attribute less regret to action than prevention-focused individuals, but the two groups attribute similar regret levels to inaction decisions. This asymmetry between action and inaction implies that a decision not to act is the default or the norm, as suggested by the norm theory (Kahneman and Miller, 1986). This means that inaction or leaving things as they are, without making any change, is the first and basic option in a situation of choice. Taking action or changing the status quo, on the other hand, is a less trivial choice and it requires more intent and deliberate plan. Therefore, inaction decisions are similarly perceived by different individuals, and even among individuals who tend to use action strategies or a promotion focus–inaction is still an acceptable and normal option. Action decisions, on the other hand, are perceived as a desirable option only by individuals who are predisposed to action; since an action decision is beyond the default and it takes more effort and intent to choose it, such a decision will not fit all individuals. This extends the norm theory of Kahneman and Miller (1986), suggesting that individual differences (at least with respect to the regulatory focus) are more notable in regret following action than in regret following inaction. We encourage future studies to further test the asymmetric effect of individual regulatory focus as well as other individual tendencies on action vs. inaction decisions.

# Prior Negative Outcome and Trigger for Change

Zeelenberg et al. (2002) suggested that when there is information regarding a prior negative outcome, action decision becomes more normal and acceptable, and therefore is less regrettable, than when no such information exists. Our results support this idea that a prior negative outcome makes action more normal than otherwise and consequently reduces regret following action. However, we go beyond this and find that not only prior negative outcomes but also other situational cues that signal the need for change, such as changing a weekly target at work (scenario 5), reduce regret following action. We suggest that a trigger for change makes action more normal than without such a trigger, and therefore it reduces regret following action, in line with the norm theory of Kahneman and Miller (1986), which suggests that regret is greater when it follows less normal decisions. Our findings add to other studies that show particular situations in which action is the norm and therefore produces less regret, such as the decision of goalkeepers in penalty kicks to jump (Bar-Eli et al., 2007). However, although our results show that a trigger for change reduces the level of regret attributed to action, it is not reversing the regret attribution pattern. In the three scenarios that contained a trigger for change (2, 5 and 6), only in scenario 2 a reversed pattern was evident (i.e., inaction was perceived as more regrettable than action). However, even in scenario 2, where the percent of attributing more regret to action is only 44.8% (the lowest among the scenarios), it is not statistically significant different from 50%.

# Practical Implications

An implementation of our results to decision making situations in both individual and organizational contexts would be to select promotion-focused individuals for decision making assignments in which actions must be made. Since there is less regret following action among promotion-focused individuals, it is more likely that such individuals will have less regret aversion and will be more willing to take action when it is needed. Examples of contexts in which action decisions are mostly preferred would be Hi-Tech industries, or organizations who operate in a dynamic and turbulent environments that require frequent changes in technology, products, human resources, and so on. Another context that requires action decisions would be an entrepreneurial environment, in which individuals must be creative and innovative, discover opportunities, and develop new products. We are not suggesting that only promotion-focused individuals are required to make decisions in such environments and contexts, but in comparison to stable environments, high doses of promotion-focused individuals would be desirable. In contrast, in stable and less dynamic environments, changes and action decisions are required less frequently, and therefore the advantage of promotion-focused individuals is less significant. Yet, as our results show, inaction decisions are generally more preferred and less regretted by all individuals, regardless of their regulatory focus. Therefore, in steady environments, we suggest that both prevention—and promotion-focused individuals will tend to prefer inaction decisions. However, this idea needs further examination in both lab and field studies.

Another practical implication for effective decision making in organizations stems from our findings regarding the effect of a "trigger for change." In order to encourage action decisions (in contexts that require changes), a useful suggestion would be to provide such triggers for change. For example, a manager who emphasizes to the employees the differences between the current situation and the previous one creates more triggers for change than a manager who emphasizes the similarities between the situations or who does not emphasize anything. As another example, consider two universities in which the Dean asks the faculty to update their courses and propose beneficial changes to the program. In the first university the Dean emphasizes that due to increased competition from colleges there is a reduced demand for the program. In the second university, although the situation is similar, the Dean just asks to try to improve the program as much as possible, or may be even emphasizes the similarities (e.g., that after the proposed changes, courses should still be semesterbased, and the BA should still take 4 years). The first Dean, who emphasizes the changes in the environment, creates a trigger for change, and therefore is likely to encourage a more proactive and innovative mindset, more changes, and more needed action than the second Dean who did not create a trigger for change. According to our findings, triggers for change reduce the level of regret from action decisions, and thus increase the tendency to adopt action decisions.

# Research Limitations and Future Research

One limitation of our study is that the regulatory focus manipulation did not produce the expected effect. The current manipulation was chosen because we observed in other studies that Israeli subjects do not react as expected to the more common manipulations for regulatory focus (Higgins et al., 2001; Freitas and Higgins, 2002), i.e., these manipulations did not create promotion and prevention foci in Israeli samples. One possible reason is different interpretation of Israelis (compared to American subjects) of the terms used in Higgins' manipulations, namely oughts, duties, and obligations vs. ideals, dreams and aspirations. The manipulation that we have used is based on similar technique used by Lockwood et al. (2002) and it was recently tested by Schödl and Van Dijk (2014). Although the manipulation was independent of the individual measure of regulatory focus (because it was randomly allocated to subjects and because it was carried out after measuring individual regulatory focus), as another precaution we controlled for the potential effect of the manipulation by adding it as an independent variable in the regressions.

Another limitation of our study is that we tested the effect of regulatory focus on regret with hypothetical scenarios rather than creating true regret in the individual. However, creating real regret in the individual is very difficult. One needs to have the subject make a decision, then to make sure the decision results in failure so that a potential regret may arise. Even then, if the individuals do not attribute the failure to a significant mistake they made, they might not feel regret. For example, if one guesses the numbers in a lottery and then does not win, he probably does not feel strong regret, because there was no way in which he could know the winning numbers. So running an experiment in which subjects make decisions and the experimenter informs them that they made a mistake and they lose, will not necessarily create regret. Furthermore, even if one can design an experiment that creates real regret in the lab, it is likely to be regret about losing small amounts of money in an artificial setting. On the other hand, with the scenarios we were able to describe situations that involve more significant regret than losing a few dollars, and with a greater diversity of situations. By using six different hypothetical scenarios in different contexts, three different measures for regret, and two different measures for individual regulatory focus, we further increase the robustness, validity and the richness of the results. Although the above arguments explain our choice of hypothetical scenarios, it is a worthwhile direction for future research, albeit not an easy one, to think about lab experiments with real consequences that induce regret and use them to analyze how personality differences in general and regulatory focus in particular affect regret. Such studies may be interesting complements to our results.

# REFERENCES

Future studies should explore the impact of one's religiosity level on regret following action vs. inaction. Our findings show that orthodox people tend to attribute more regret than nonorthodox to a person who made an action decision. One explanation could be that orthodox people are more conservative and oriented to keep the status quo and avoid changes and risks. However, this finding is found only when using the dichotomous measure of regret and was not replicated with other measures of regret. Therefore, more research is needed in order to verify this effect.

Further research can be useful in order to verify our findings about the influence of regulatory focus on regret and confirm it in diverse situations, with different samples of subjects. We suggest to further explore the asymmetric effect of regulatory focus on action vs. inaction. An interesting direction would be to examine whether inaction is a type of decision that is perceived as the norm by most individuals, regardless of their personality, whereas an action decision is perceived differently according to the individual tendency, because it is considered as a less normal strategy. Another direction could be to present to the subjects various scenarios in different orders and analyze whether the order makes a difference. Additionally, the trigger for change should be tested in future studies in order to clarify and identify what types of information are perceived as a trigger for change, and consequently weaken the general tendency to regret more action than inaction decisions. Finally, the interaction effect that was found between the trigger for change and regulatory focus calls for future research to explore whether (and in what conditions) a trigger for change, which signals deviation from the norm, increases the impact of individual differences on regret feeling.

# AUTHOR CONTRIBUTIONS

AI: Experimental and study design, Analysis of the results, Writing the article. DV: Experimental and study design, Analysis of the results, Writing the article. OA: Experimental and study design, Analysis of the results, Writing the article.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Itzkin, Van Dijk and Azar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# APPENDIX: THE SCENARIOS USED IN THE EXPERIMENT

# Scenario 1: Stock Investment (Kahneman and Tversky, 1982)

Paul owns shares in Company A. During the past year he considered switching to stock in company B, but he decided against it. He now finds out that he would have been better off by \$1200 if he had switched to the stock of Company B. George owned shares in Company B. During the past year he switched to stock in Company A. He now finds out that he would have been better off by \$1200 if he had kept his stock in Company B. Who feels more regret?

# Scenario 2: Soccer Teams (Zeelenberg et al., 2002)

Jacob and Noah are both coaches of a soccer team. Jacob is the coach of team A, and Noah is the coach of team B. Both coaches lost the prior game with a score of 4–0. This Sunday Jacob decides to do something: He fields three new players. Noah decides not to change his team. This time both teams lose with 3–0. Who feels more regret, coach Jacob or coach Noah?

# Scenario 3: Project Management

Shirley and Rene are both project managers in a global company. As part of their jobs they decide with which projects to continue and which to terminate every quarter based on performance. At the beginning of the year, both of them were required to make a decision regarding projects that started earlier. Shirley decided to terminate project A and switch it with project B. Rene on the other hand decided to continue with project C that she started earlier. At the end of the year it turned out that both projects B and C failed, produced losses, and it was decided to terminate them.

	- b. What is the level of regret that Rene feels on a scale of 0–100 (0 - no regret at all, 100 - very high level of regret)?

# Scenario 4: Supplier Choice

Emma and Mia both work as purchasing managers in a big pharmaceutical company. As part of their jobs they decide with which raw materials suppliers to work. The company has been purchasing a variety of raw materials for the past five years from supplier A. Emma needed raw material X and received for it offers from both supplier A and supplier B, who is a supplier that has not yet been working with the company. Mia needed raw material Y and received for it offers from both supplier A and supplier C, who is a supplier that has not yet been working with the company. Emma decided to purchase the raw material X from the new supplier B. Mia decided to purchase the raw material Y from the old supplier A. After some time it was discovered that both new raw materials X and Y, from both suppliers B and A respectively, were of low quality and caused the company losses.

	- b. What is the level of regret that Mia feels on a scale of 0–100 (0 - no regret at all, 100 - very high level of regret)?

# Scenario 5: Machine Parameters

Michael and Daniel are both machine operators in a company that manufactures plastic products. Every week each of them receives his weekly target and has to make sure that the machine under his responsibility will produce this target. This week the target was higher than usual for both of them and therefore Michael and Daniel pondered what to do. Michael decided to change the machine parameters. Daniel decided to stay with the regular parameters. At the end of the week both Michael and Daniel did not succeed to reach the weekly target.

	- b. What is the level of regret that Daniel feels on a scale of 0–100 (0 - no regret at all, 100 - very high level of regret)?

# Scenario 6: Academic Studies (Based in Part on Gilovich and Medvec, 1995)

Roy and Alex both studied for a Bachelor's degree in management and decided to continue to a Master's degree in business administration in the same university. After a short period in the degree both Roy and Alex felt that the degree is not contributing to them and the general feeling was that the attitude towards them is unpleasant and they do not enjoy the degree. Roy and Alex each considered whether to quit the university for a similar track in another university. Roy decided to stay and Alex decided to move to a different university. After half a year, they met and updated each other. They found that both of them are still unsatisfied with the degree they study.

	- b. What is the level of regret that Alex feels on a scale of 0–100 (0 - no regret at all, 100 - very high level of regret)?

# Don't Always Prefer My Chosen Objects: Low Level of Trait Autonomy and Autonomy Deprivation Decreases Mere Choice Effect

Zhe Shang, Tuoxin Tao and Lei Wang\*

Department of Psychology and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

Choice effect is a robust phenomenon in which even "mere choice" that does not include actual choosing actions could result in more preference for the self-chosen objects over other-chosen objects. In the current research, we proposed that autonomy would impact the mere choice effect. We conducted two studies to examine the hypothesis. The results showed that the mere choice effect measured by Implicit Association Test (IAT) significantly decreased for participants with lower levels of trait autonomy (Study 1) and when participants were primed to experience autonomy deprivation (Study 2). The theoretical and practical implications are discussed.

Keywords: mere choice effect, object evaluation, autonomy, self-enhancement, cognitive bias

#### Edited by:

Aurora García-Gallego, Universitat Jaume I, Spain

#### Reviewed by:

Wing-Yee Cheung, University of Southampton, UK Eric Mayor, University of Neuchâtel, Switzerland

> \*Correspondence: Lei Wang<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology

> Received: 31 December 2015 Accepted: 29 March 2016 Published: 19 April 2016

#### Citation:

Shang Z, Tao T and Wang L (2016) Don't Always Prefer My Chosen Objects: Low Level of Trait Autonomy and Autonomy Deprivation Decreases Mere Choice Effect. Front. Psychol. 7:524. doi: 10.3389/fpsyg.2016.00524 INTRODUCTION

People make choices according to their preferences, indicating the important role that preferences play in choices.  After the choice has been made, people's liking of the chosen objects tends to increase while that of the rejected objects tends to decrease, known as the post-decisional spreading of alternatives (Brehm, 1956; Hammock and Brehm, 1966). In other words, people would prefer one of two similar objects simply because they chose one rather than the other, which is also known as the choice effect (Huang et al., 2009). Since the initial work of Brehm (1956), this phenomenon has got widespread attention (Ariely and Norton, 2008). The choice-induced preference has been found to exist strongly in several forms, such as in real choice actions (Patall et al., 2008) and illusory choices (Huang et al., 2009).

Over the past decades, the cognitive dissonance theory and self-concept related theories have been widely used in explaining the mechanism underlying the choice-induced preferences. The main classical explanation is based on the cognitive dissonance theory (Festinger, 1957), which argues that people are motivated to maintain internal consistency between cognitive inputs and behavioral outputs to reduce the uncomfortable feeling of dissonance. An individual is likely to experience cognitive dissonance if he/she holds negative attitudes toward an object, given that the object has been chosen by oneself, because thus a conflict would occur between the cognitive input ("I don't like this thing") and the behavioral output ("I chose this thing") (Van Overwalle and Jordens, 2002). In order to reduce this uncomfortable feeling of cognitive dissonance, individuals would increase their liking of the chosen objects when the actual choice action has been taken (Olson and Stone, 2005). This theory helps to explain why people prefer a chosen object to an unchosen one simply because they took an explicit action to choose the object. However, when there is no explicit choosing action, the cognitive dissonance theory loses its power in explaining

the mechanism of the choice-induced preference. Recent empirical research has indicated that the choice effect happens even when individuals lack the awareness of their choosing behaviors (Lieberman et al., 2001; Coppin et al., 2010), suggesting that cognitive dissonance may not be a necessary prerequisite of choice-induced preference. Indeed, choice-induced preferences are found even when the choices were seemingly trivial (Langer and Rodin, 1976) or wholly illusory (Langer, 1975). That is, choice-induced preferences existed even when there was no explicit choice action and thus the awareness of cognitive dissonance may not be present. The phenomenon where choice itself is powerful enough to induce liking, even in the condition that choosing is illusory and does not actually occur, was termed by Huang et al. (2009) as the mere choice effect.

The theory referring to the positive valence of self node helps to explain the choice effect as well (Greenwald et al., 2002). "Self node" means that self was treated as a node in the selfrelated concept tree. "Self " is the sum of all that one can call his/her own (James, 1890). "My choice" is also a part of the selfconcept. Theories and phenomena associated with self-serving or self-protecting biases (Sedikides and Strube, 1997), such as self-enhancement (Kurman, 2001), self-affirmation (Brown and Dutton, 1995), and self-verification (Chen et al., 2006) imply that people are prone to evaluate "my choice" as better than "others' choice" to maintain a positive self-image, and thus would display a positive evaluation on self-chosen objects. "Self node" affects the choice-preference link by increasing preference on self-chosen objects in an implicitly way, which leads to mere choice effect. On the other hand, to choose is to express a preference and to assert the self (Leotti et al., 2010). Consequently, attaching a high evaluation to "my choice" implies acceptance of the self and thus in turn brings higher self-satisfaction and selfesteem. The scope of self-concept is broader than just one's possessions or decisions (choices). As the Ryan and Deci (2000) theorized self-determination theory (SDT), the need for competence, autonomy, and psychological relatedness are three psychological needs that motivate the self to initiate behavior (Deci and Ryan, 1985, 2000). The act of self-regulation, such as autonomy, is also related to self-concept. Experiencing autonomy promotes the sense that an individual's behavior is self-motivated and self-determined and thus maintain a positive self-image. Applied in the objects evaluation, another possible theoretical explanation refers to the role that autonomy plays in the choice effect.

The sense of autonomy refers to the extent to which people feel free to make their own decisions and experience a sense of volition in their actions (van Prooijen, 2009). Choosing behavior increases the experience of autonomy by allowing people to exert their right to make a decision. Previous research has demonstrated that people evaluate the chosen alternative as more desirable than the rejected alternative, in order to reassert their autonomy (Hammock and Brehm, 1966). Experiments have suggested that manipulations designed to enhance one's experience of autonomy can boost intrinsic motivation and energize behavior (Swann and Pittman, 1977; Zuckerman et al., 1978; Simon and McCarthy, 1982, Unpublished). Offering people an optimal amount of choice enhanced their intrinsic motivation and energy to persist (e.g., deCharms, 1968; Deci and Ryan, 1985). As demonstrated by plenty of research, autonomy is associated with intrinsic motivation (Deci et al., 1999), persistence (Moller et al., 2006), goal attainment (Sheldon and Elliot, 1998), and creativity (Sheldon, 1995), indicating that autonomy elicits positive outcomes. Additionally, perceived autonomy has an effect on enhancing happiness (Chekola, 2007; Demir et al., 2011), job satisfaction, and a general increase in subjective well-being (Sheldon et al., 2004), all of which conclude that autonomy elicits positive personal feelings. Preference for an object represents the positive objective valence that one attaches to the object in the process of evaluation. As has been validated by previous research, personal positive state and feelings influence evaluation, in terms of increasing personal preference/sensitivity on surroundings and targeted objects (Gu et al., 2010; Yang et al., 2014). It is possible that the sense of autonomy elicited by choosing enhances an individual's evaluation toward an object, because experiencing autonomy induces positive feelings, which in turn have a positive effect on the evaluation of the object.

We speculate that the experience of autonomy may enhance the preference on self-chosen objects. Thus, we measured the relationship of trait autonomy and choice effect in Study 1 and propose the hypothesis: (1) trait autonomy is positively correlated with choice effect. We infer that autonomy may moderate the choice-preference link. When an individual takes a choosing action or is simply acknowledged that something has been chosen by himself/herself, the sense of autonomy is generated, which brings him/her positive feelings. These positive feelings in turn may enhance his/her positive evaluation to the surroundings. On the contrary, the lack of autonomy may reduce the preference for self-chosen objects. To our best knowledge, however, no study has provided empirical evidence for the role of autonomy in the choice effect. In Study 2, we investigated the influence of different levels of autonomy experience on the choice effect by using a priming task to set three conditions: the autonomy fulfillment condition, the autonomy deprivation condition, and the control condition. Here we propose the hypothesis: (2) the choice effect would occur in the autonomy fulfillment condition and in the control condition, but not in the autonomy deprivation; (3) autonomy deprivation would decrease or even eliminate the choice effect when compared with the autonomy fulfillment condition; (4) autonomy deprivation would decrease or even eliminate the choice effect when compared with the control condition; (5) autonomy fulfillment would increase the choice effect when compared with the control condition.

# Overview of Two Studies

In order to study the influence of autonomy on the choice effect while excluding the impact of cognitive dissonance, we employed a modified illusory choice paradigm, adapted from Huang et al. (2009) to measure the presence of the mere choice effect. We adopted the Implicit Association Test (IAT, Greenwald et al., 1998) that records the response time when participants respond to settled categories of objects framed by positive or negative adjectives.

In measuring autonomy, we treated it as an individual difference variable (Deci and Ryan, 2000). It can be either dispositional or situational. Thus, we tested our hypothesis through two studies. In Study 1, we recorded participants' selfreport trait autonomy and divided participants into high and low autonomy groups accordingly. Study 2 adopted a priming paradigm to manipulate the situational autonomy in three levels.

# STUDY 1: THE RELATIONSHIP BETWEEN TRAIT AUTONOMY (BETWEEN-SUBJECT VARIABLE) AND THE MERE CHOICE EFFECT

In Study 1, we used a scale to measure trait autonomy as an individual differential variable. Subsequently, we tested the mere choice effect using an IAT paradigm. We then calculated the relationship of trait autonomy and the mere choice effect.

# Materials and Methods Participants

A total of 91 graduate and undergraduate students (50 female, 41 male, average age = 22.2 years, SD = 2.39, ranging from 19

to 25 years old) participated in the experiment for a cash reward (US\$2). We asked all the participants to conduct an object chosen task in which they would see some texts in a computer screen and react by push some buttons on the keyboard. Each of them wrote informed written consent before the test. They were told there would be no any dangers while they were doing the experiment. They were told their rights and they can decide to or not to participate in this experiment, and they had the right to quit the experiment at any time of the experiment. This study was in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Department of Psychology, Peking University.

## Procedure and Materials

Participants were told that there were two unrelated tasks. After each experiment session, we asked the participant whether he/she thought the two parts were related. None of them replied yes. In the first task, they were required to complete questionnaires including trait autonomy and demographic survey. The second task was a mere choice task presented on computer, adapted from Huang et al. (2009), which created a mere choice situation to the participants. As previous research has demonstrated (Huang et al., 2009), when participants are asked to choose something for a third party, they would implicitly prefer the self-chosen object to the other-chosen objects (i.e., the choice effect).

### Trait Autonomy

The five-item Choicefullness Subscale of the Self Determination Scale (Sheldon, 1995; Sheldon et al., 1996) was used to measure trait autonomy. Each item presented participants with two opposing statements. Participants were asked to indicate which of the two statements was more appropriate for describing themselves. An example item is showed as follows: "I always feel like I choose the things I do" (Statement A) versus "I sometimes feel that it's not really me choosing the things I do" (Statement B) (5-likert scale: 1 = only A feels true; 5 = only B feels true). The answers were coded such that lower scores indicated lower level of autonomy. Our data showed good internal consistency (Cronbach's α = 0.697)

# Mere Choice Effect

To evaluate the mere choice effect (an affect reflected the degree of preference on self-chosen objects over other-chosen objects), both of the two studies used a modified illusory choice paradigm developed by Huang et al. (2009), in which participants were asked to imagine a scenario about choice instead of taking an actual choice action.

This part was completed on computers using the Inquisit laboratory software.

In Step 1, participants read the following two-page scenario on the computer screen:

Please visualize the following scenario.  Please visualize and remember these products. They will be used in the following experiment (Page 1).

Among these six products, three of them were chosen by you, and the other three were chosen by the friend (the other) shopping with you. You chose the mug, the chocolate, and the pen. Your friend chose the small figurine, the candy, and the ruler. Please spend 2 min to visualize and separately remember your choices and your friend's choices. They will be used in the following experiment (Page 2).

Half of the participants were shown the aforementioned scenario. The other half read similar instructions except that we swapped the products assigned to the self and the other.

Then the participants began the modified illusory choice IAT (Huang et al., 2009). This IAT followed the procedure designed by Greenwald et al. (1998), involving two target categories (objects chosen by the self vs. objects chosen by the other) and two attribute categories (positive vs. negative). Target categories followed the scenario described previously. The attribute categories were previously used in many studies (Maison et al., 2004; Huang et al., 2009). The positive stimuli included the Sun, luck, love, fun, happiness, pleasure, holiday, and friendship. The negative stimuli included disease, death, murder, accident, poison, war, tragedy, and vomit. Our study was consistent with the classical IAT paradigm (Lane et al., 2007), target words and attribute words were presented together in the IAT paradigm. In the two main tasks of IAT, there were two situations: in one situation, the words "selfchosen objects/positive attributes" appeared in the top lefthand corner while the words "other-chosen objects/negative attributes" appeared in the top right-hand corner; in the other situation, the words "self-chosen objects/negative attributes" appeared in the top left-hand corner while the words "otherchosen objects/positive attributes" appeared in the top righthand corner. As can be seen, in both situations, the selfchosen and other-chosen objects appeared together as the

target words, making it impossible to analyze their effects separately.

The IAT consisted of five classification tasks (see **Table 1**): attribute discrimination task (Block 1, 24 trials), initial targetcategory discrimination task (Block 2, 24 trials), initial combined task (Block 3, 24 trials for practice, and Block 4, 48 trials for data collection), reversed target-category discrimination task (Block 5, 48 trials), reversed combined task (Block 6, 24 trials for practice and Block 7, 48 trials for data collection).

In the attribute discrimination task (Block 1, 24 trials), participants were asked to press a left key (F) when a positive word appeared on the screen and a right key (J) for a negative word. Similarly, in the initial target-category discrimination task (Block 2, 24 trials), objects chosen by the self (responding by pressing the left key) and objects chosen by the other (responding by pressing the right key) were discriminated. In the initial combined task (Block 3, 24 trials for practice and Block 4, 48 trials for data collection), attribute and target discrimination trials were combined and participants had to press the left key when either a positive word or an object chosen by the self was presented and the right key when a negative word or an object chosen by the other was presented (the compatible condition, we replicated the IAT paradigm in accordance with a previous study (Huang et al., 2009), in which the participants' responses showed that self-chosen objects were implicitly linked with positive words (e.g., happiness, sunshine), as opposed to negative words (e.g., death, war), and in which other-chosen objects were implicitly linked to negative words, as opposed to positive words. Thus, we argue that the compatible condition was composed of self-chosen objects with positive descriptions and other-chosen objects with negative descriptions, just as Huang et al., 2009 showed). In the reversed target-category discrimination task (Block 5, 48 trials), Block 2 was repeated with a switch of the categorization keys by pressing left key when an object chosen by the other appeared on the screen and a right key when an object chosen by the self appeared. The reversed combined task (Block 6, 24 trials for practice and Block 7, 48 trials for data collection) again combined two individual tasks. Participants were instructed to press the left key when either a positive word or an object chosen by the other was presented and press the right key when a negative word or an object chosen by the self was presented (incompatible condition). Each block started with a brief instruction for the following task and a request to respond as fast as possible while trying to minimize mistakes. Participants were also reminded that their error rate and response times would be recorded.

Different random orders of trails were used for different participants. Half of the participants went through the seven blocks in the order presented previously; to remove any order effect, Blocks 2, 3, and 4 were swapped with Blocks 5, 6, and 7 for the other half of the participants. Only data from Blocks 4 and 7 were used for analysis. Each block started with a brief instruction.

After each experiment session, the participant was fully debriefed, thanked, and paid for his/her participation.

# Results

We analyzed the data following the processes suggested by Greenwald et al. (1998). The first two trials of each block were excluded since the response latencies for them were typically longer. Next, we recoded the latencies by excluding reaction times (RTs) that were below 300 ms or above 3000 ms, so that we could control for outlying trials where distraction and anticipation likely affected the trial. We disregarded any participant with an error rate above 30%. Thus, our final data analysis included 87 participants (46 female, 41 male, average age = 21.1 years, SD = 2.36, ranging from 18 to 25 years old).