Edited by: Ullrich Wagner, Charité – University Medicine Berlin, Germany
Reviewed by: Leonie Koban, University of Colorado Boulder, USA; Job Van Der Schalk, Cardiff University, UK
*Correspondence: Alessandro Grecucci, Department of Cognitive Science and Education, University of Trento, Corso Bettini 31, Rovereto, 38068 Trento, Italy. e-mail:
This article was submitted to Frontiers in Emotion Science, a specialty of Frontiers in Psychology.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
Emotion regulation is important for psychological well-being. Although it is known that alternative regulation strategies may have different emotional consequences, the effectiveness of such strategies for socially driven emotions remains unclear. In this study we investigated the efficacy of different forms of reappraisal on responses to the selfish and altruistic behavior of others in the Dictator Game. In Experiment 1, subjects
Recent experimental evidence suggests that emotion regulation strategies play a key role in helping individuals to adapt to and master social interactions (Gross,
These socially driven emotions have been recently explored by asking about the emotional regulation of subjects when looking at pictures depicting social scenes (Koenigsberg et al.,
To study real interactive situations, one popular approach has been to examine emotion regulation strategies applied to tasks derived from Game Theory. Game theory explores situations of conflict and cooperation between decision-makers (Myerson,
Here, we aim to extend the above study by testing how social norms (such as fairness, equality, and prosocial behavior), and in particular their violations, affect our emotional reactions in an interactive context. The Grecucci et al. (
A further issue to examine here is whether different strategies have similar effects on the regulation of socially driven emotions. Of the set of strategies studied in the experimental literature of self regulation, the most well-characterized is that of reappraisal. This strategy involves reinterpreting the meaning of a stimulus in order to change one’s emotional response to it (Gross,
A final unresolved issue is whether emotion regulation acts upon valence, upon arousal, or on both, and more importantly, if arousal can be decreased or increased according to the valence of the experienced emotion. The vast majority of previous studies (Ochsner et al.,
Secondly, we examine whether different strategies are equally effective in promoting emotion regulation. Therefore, we will test two different emotion regulation strategies: mind-of-another-reappraisal, or
Thirdly, we will test whether emotion regulation when interacting with a human partner is different when interacting with a non-human partner. In both contexts the strategy is the same, applied to monetary offers from both human and computer donors respectively. We expect that interpersonal emotion regulation is superior when reappraising the emotions elicited from a human as opposed to a non-human partner due to the “mentalistic” nature of the strategy used. If this is the case, this will be further confirmation of the importance of interpersonal abilities on emotion regulation of socially driven emotions, as predicted by theory (Fonagy,
Fourthly, across both experiments we will examine both arousal and valence dimensions, examining differences in how alternative emotion regulation strategies (Experiment 1) and alternative contexts (Experiment 2) can affect our emotional experience. Previous experiments did not make a clear distinction between valence and arousal effects of emotion regulation. In addition to the effect of strategy on the perceived valence we also expect an effect on arousal, as it is an important dimension of the emotional experience. In particular, we predict different effects on valence and arousal according to the specific strategy used. As mentalizing involves the reinterpretation of the event, we expect a strong change on the perceived valence, but less on arousal. On the contrary, as distancing is more focused on putting oneself in a detached perspective, here we expect a stronger effect on the arousal dimension, and less on valence, as no cognitive operation is required for the evaluation of the event.
This experiment will examine the effect of regulation on socially driven emotions by employing two strategies, those of mentalizing and distancing.
Twenty-two participants (11 males) from the local area participated in the study, with a mean age of 23.95 (SD ± 1.43) years. The local ethics committee approved the study and all participants provided written informed consent after the procedures had been fully explained.
After providing informed consent, participants were first instructed as to the nature and rules of the Dictator Game. Participants were told that they would play this game as recipient with a different player in the role of the allocator on each trial. Sixty trials were presented, though participants were not informed of the total number of rounds in advance. Each round involved receiving a proposal concerning a 10€ amount. The offers included four repetitions of five possible offers (1€, 2€, 3€, 4€, and 5€ out of 10€), for a total of 20 offers for each of the three conditions (look, mentalizing, and distancing). The emotion regulation conditions were blocked and counterbalanced across participants. This was done to prevent any substantial task switching and carry over effects from one strategy to another. The offer types and pictures of Allocators were completely randomized inside each block. The task instructions emphasized that the different partners in the game would play the game independently of each other, and participants were led to believe the offers were previously recorded from real partners. Participants played the game using a computerized version of the task. The timeline of each run involved the presentation of a fixation point for 500 ms, then the instruction of the regulation strategy to be applied appeared for 2000 ms, followed by the face of the proposer and the proposal itself for 8000 ms, leaving the time to apply the strategy. After this, they were asked to rate their emotions separately on two scales (one for arousal and one for valence) using a visual analog scale known as the Self Assessment Manikin (Lang,
Before beginning the game, participants were instructed that they would have to use specific cognitive strategies upon the receipt of an offer. A written protocol describing each of the two strategies was provided. Following Gross (
For the other strategy, distancing, they were told that how involved they feel in a situation will affect their perceived distress. A picture was then presented depicting a bloody fight between police and terrorists, and they were told that if they feel themselves affected by this situation they probably will feel scared and worried, whereas if they think that that situation is far from their lives and not connected at all with them, they will feel quite neutral in relation to that event. After this, subjects were told how to apply this strategy to the context of DG. Some examples were given, such as (“
Importantly, distancing was meant to be an avoidance-based strategy, meaning that subject had to put themselves in a detached perspective, whereas mentalizing was meant to be an effort of connection with the others. Finally, for the “look” condition, they were to simply allow themselves to respond naturally, without any effort of interpretation.
Before beginning the first block of DG, we verified that participants understood the respective emotion regulation instructions by asking each to verbalize what they would do when confronted with different offers. A practice session proceeded every block.
At the conclusion of the experiment, participants were asked to rate their emotional state on a 9-point Likert scale when they received the prototypical example of a very unfair offer (1€ out of 10€), and fair (5€ out of 10€). Moreover, effectiveness of change of emotional responses for both strategies was rated again on a 9-point Likert scale. Thinking strategies adopted during the experiment were also recorded for both strategies. This was done to ensure that participants understood the instructions and then applied them in a coherent manner according to the training instructions.
We first examined if the affective ratings were different across the emotion regulation and baseline conditions. We computed two separate ANOVAs, one for valence and one for arousal, each with Strategies (mentalizing vs. distancing vs. look), and Offers (1€, 2€, 3€, 4€, and 5€) as factors. Analysis on valence returned a significant main effect of Strategy [
Then, we computed ANOVA on arousal ratings. This returned a significant main effect of Strategy [
To further explore the effect produced by each strategy, we computed the effect size of each strategy, calculated as the difference between the strategy and the baseline look condition, collapsing for all offers. In terms of valence, the mentalizing strategy returned a strong effect of 1.51 points toward more positive perception of the interaction with the partner, whereas distancing was less effective, producing a small effect of −0.19 in the direction of perceiving the events as less positive. In terms of arousal, the mentalizing produced an effect of 0.8 points in the direction of perceiving the emotions as more vivid, whereas the distancing strategy returned an effect of −0.51 points toward a more blunted perception of emotion. As expected, mentalizing had a stronger effect on valence as compared to distancing. When considering arousal, both strategies were effective in altering the ratings, however, they acted in opposite directions. Mentalizing increased arousal, whereas distancing decreased it. See Figure
Experiment ratings | |||
---|---|---|---|
Regulation of valence |
|||
Look | Mentalizing | Distancing | |
€1 | 2.27 (1.19) | 4.72 (1.75) |
2.92 (1.51) |
€2 | 3.04 (1.29) | 5.21 (1.58) |
3.29 (1.38) |
€3 | 3.81 (1.37) | 5.37 (1.60) |
3.75 (1.27) |
€4 | 5.45 (1.77) | 6.22 (1.72) | 4.72 (1.49) |
€5 | 6.76 (2.15) | 7.35 (1.94) | 5.68 (1.70) |
Effect size of valence | +1.51 |
−0.19 |
|
€1 | 4.89 (2.48) | 5.78 (1.98) | 4.70 (2.78) |
€2 | 4.72 (2.12) | 5.57 (1.84) | 4.25 (2.23) |
€3 | 4.71 (1.89) | 5.54 (1.75) | 4.21 (1.99) |
€4 | 5.30 (2.01) | 5.96 (1.97) | 4.43 (2.08) |
€5 | 6.02 (2.31) | 6.38 (2.11) | 4.82 (2.22) |
Effect size of arousal | +0.8 |
−0.51 |
|
Anger | 5.45 (2.80) | Anger | 1.45 (0.91) |
Disgust | 5.04 (2.53) | Disgust | 1.77 (1.19) |
Surprise | 4.54 (2.38) | Surprise | 5.90 (2.18) |
Happiness | 2.22 (1.65) |
Happiness | 7.09 (1.63) |
Sadness | 5.09 (2.58) | Sadness | 1.72 (1.24) |
Disappointment | 6.27 (2.31) | Disappointment | 1.5 (0.74) |
Emotional ratings when receiving both very fair (€5) and very unfair (€1) offers were entered into an ANOVA for each of the six emotions inquired about (anger, disgust, surprise, sadness, happiness, and disappointment). Analysis returned a significant main effect of offer [
For the fair offer, the strongest emotion elicited was happiness (7.09), followed by surprise (5.9) disgust (1.77), sadness (1.72), disappointment (1.5), and anger (1.45). Happiness and surprise differed from all other emotions (
We can therefore conclude that the main emotions elicited by the interpersonal context of the Dictator Game when treated unfairly was primarily disappointment, with disgust, sadness, and anger invoked to a lesser extent. These emotions may be the ones regulated during the strategy of mentalizing. We can also conclude that the main emotion elicited by fair treatment was mainly happiness, but also surprise was invoked by the altruistic behavior. See Figures
After the experiment, participants were also asked to evaluate on a 9-point Likert scale how much they felt their emotions changed as a function of the two emotion regulation strategies. In the mentalizing condition they rated their emotion change with strength of 5.54 (SD ± 2.17) when confronted with selfish behavior, and 5.41 (SD ± 2.30) when confronted with altruistic behavior. In the distancing condition they felt their emotions changed with a strength of 4.59 (SD ± 2.30) when confronted with selfish behavior, and 4.22 (SD ± 2.24) when confronted with altruistic behavior. Emotional ratings when applying the two strategies to both fair (€5) and unfair (€1) offers entered an ANOVA. Analysis returned a significant main effect of strategy [
The aim of this first experiment was twofold. Firstly, we wanted to test whether emotion regulation can be applied in an interpersonal context to complex social emotions, as opposed to the simple visual stimuli used in previous studies. Secondly, we examined whether two different emotion regulation strategies, mentalizing and distancing, can affect emotion perception in an interactive context in which people observed selfish and altruistic behavior regarding the splitting of a pot of money. Our data demonstrate that interpersonal emotion regulation is possible, and indeed strongly affects our perception of both selfish and altruistic behaviors. Importantly, mentalizing (e.g., reinterpretation of the intentions of the players in a way to make them less negative) increased the valence (more positive) of selfish economic offers (in the range of €1–€3 out of 10). Conversely, distancing (e.g., considering events with a detached perspective) did not affect the negative emotions elicited by selfish offers, but paradoxically decreased the valence of emotions elicited by the altruistic offer of €5. Questionnaires confirmed this observation, and suggested that the emotion regulated by the strategies was disappointment (higher values) but also other unpleasant emotions when treated selfishly, and happiness and surprise when treated altruistically. Interestingly, analyses on arousal revealed that mentalizing not only increased the valence of the offers leading recipients to consider them as more positive, but also increased the arousal associated with them (size effect of valence of Figure
On the contrary, distancing failed to increase the valence for negative emotions (elicited by selfish proposals), but also decreased the valence of positive emotions elicited by altruistic proposals (offer 5€). In other words, recipients failed to alter the meaning of the proposals. Notably this also affected arousal, but this time decreasing the strength of emotions (size effect of arousal – Figure
The aim of Experiment 2 was to test whether emotion regulation is different when applied in social and non-social situations. Participants played the Dictator Game, but with both human (in a similar fashion to Experiment 1) and computer partners. Participants were trained to apply reappraisal when facing human and computer partners. The strategy was the same (cognitive reinterpretation of the event in a way to make it less negative) but with a focus on the intentions in case of a human partner, and a focus on situation when the partner was a computer. We predicted both strategies are effective in altering the emotional experience. However, we expected a stronger effect for interpersonal regulation (greater differences between human and computer in reappraisal condition than in look condition).
Twenty-four participants (10 males) from the local population participated in the study, with a mean age of 22.91 years (SD ± 4.77). The local ethics committee approved the study and all participants provided written informed consent after the procedures had been fully explained.
The Dictator Game as described above was used, with the only difference that a computer image was presented in the computer condition instead of a face. Participants were told that proposals in the computer condition were randomly generated. Again, each round involved receiving monetary proposals, with each trial dividing 10€. The offers included four repetitions of five possible offers (1€, 2€, 3€, 4€, and 5€ out of 10€), for 20 offers for each of the four conditions (Look vs. Reappraisal, Human vs. Computer), for a total of 80 trials. Type of offers and partners (Computer vs. Human) were completely randomized inside each block, whereas the strategies were separated into two blocks. To encourage engagement in the task it was emphasized that they would be paid a percentage of what they received during the game. Again participants rated their emotions separately on two scales (arousal and valence).
Before beginning the game, participants were told that they would use a specific cognitive strategy upon receipt of any offer. A written protocol describing reappraisal was provided, very similar to that of Experiment 1, with the exception that the distancing strategy was omitted and also that examples were given as to how to apply reappraisal in both contexts (human vs. computer). To apply reappraisal to a human partner they were asked to focus on the mind of the player, building an interpretation of the intentions behind their behavior. This reinterpretation of their intentions was meant to be less negative. Some examples were then given (“
Before beginning the first block of DG, we verified that participants understood the respective emotion regulation instructions by requiring them to verbalize what they would do when confronted with different offers. A practice session proceeded every block.
At the conclusion of the experiment, participants were asked to rate their emotional state when they received the prototypical example of a very unfair (€1 out of €10), and fair offer (€5 out of €10) separately for computer and human partners. Moreover, we asked the strength of perceived emotions when receiving the unfair offer for all conditions (Human vs. Computer, Look vs. Reappraisal). To check for differences on perceived effect of reappraisal between the human and computer partners, at the end of the experiment we asked for ratings on a 9-point Likert scale as to how much they felt their emotion change, for both interacting with a human and with a computer partner. An example of the precise strategies adopted during the experiment was also recorded for every participant (for both strategies) after the experiment.
Additionally, participants completed the Interpersonal Reactivity Index (IRI, Davis,
We first examined if the affective ratings were different across regulation strategies. We computed two separate ANOVAs, one for valence and one for arousal each with reappraisal Strategies (reappraisal vs. look), Partner (human vs. computer), and Offer type (1€, 2€, 3€, 4€, and 5€) as factors. Analysis on valence returned a significant main effect of Strategy [
Partner × Strategy contrasts were all significant (computer-look vs. human-look, computer-reappraisal vs. human-reappraisal; computer-look vs. human-reappraisal, computer-reappraisal vs. human-look,
Then, we computed ANOVA on arousal that returned a significant main effect of Partner [
To test the hypothesis of a stronger effect of reappraisal in changing the perceived valence for human as compared to computer offers, we computed the effect size of valence change separately for each condition. This measure was calculated as the difference between perceived valence when attending a human vs. a computer on one hand, and when reappraising a human vs. a computer on the other. We predict larger differences when applying the reappraisal strategy than when simply looking at different partners. While the difference in the look condition between playing with a computer compared with a human was 1.11 points, the difference between partners in the reappraisal condition was of 2.53 points, meaning that reappraisal doubled the difference between playing with a human or with a computer (see Figure
Experiment ratings | ||||
---|---|---|---|---|
Valence ratings |
||||
Computer-look | Computer-reappraisal | Human-look |
Human-reappraisal |
|
€1 |
2.11 (1.17) | 4.06 (1.41) | 2.36 (1.18) | 4.55 (1.50) |
€2 |
3.02 (1.29) | 4,77 (1.53) | 3.06 (1.35) | 5.01 (1.25) |
€3 |
3.67 (1.19) | 5.05 (1.17) | 3.94 (1.24) | 5.48 (1.48) |
€4 |
4.86 (1.11) | 5.77 (1.26) | 5.25 (1.22) | 6.44 (1.15) |
€5 |
6.26 (1.30) | 6.71 (1.51) | 6.56 (1.16) | 7.39 (1.37) |
€1 | 4.60 (2.11) | 4.65 (1.79) | 5.43 (2.16) | 5.76 (1.74) |
€2 | 4.76 (1.69) | 4.69 (1.53) | 5.07 (1.72) | 5.43 (1.80) |
€3 | 4.53 (1.46) | 4.94 (1.58) | 5.07 (1.68) | 5.72 (1.92) |
€4 | 4.55 (1.57) | 5.06 (1.77) | 5.55 (1.48) | 5.69 (1.91) |
€5 |
4.97 (1.75) | 5.43 (2.01) | 5.81 (1.66) | 6.14 (2.20) |
Disappointment | 6.20 (2.22) |
5.20 (2.37) | 1.5 (0.78) | 1.5 (1.02) |
Anger | 4.33 (2.07) | 3.95 (2.29) | 1.62 (1.34) | 1.83 (1.40) |
Disgust | 3.54 (2.26) |
2.16 (1.85) | 1.29 (0.85) | 1.25 (0.67) |
Sadness | 5.16 (1.97) | 4.5 (2.53) | 1.5 (0.83) | 1.66 (0.81) |
Surprise | 4.04 (2.42) | 4.45 (2.35) | 6.12 (2.13) | 5.5 (2.39) |
Happiness | 1.75 (1.18) | 1.83 (1.01) | 7.20 (1.35) |
6.41 (2.18) |
Subjective ratings when receiving the most fair (€5) and unfair (€1) offers were entered into an ANOVA for each of the six queried emotions (anger, disgust, surprise, sadness, happiness, and disappointment) for both human and computer partners. Analyses returned a significant main effect of Partner [
Next, we ran Fisher-corrected
For the altruistic fair offers, only happiness was stronger for human than computer partners (
Analysis of questionnaires revealed a positive correlation between the reported frequency of reappraisal usage in daily life (ERQ-reappraisal subscale) and the ability to take the psychological point of view of others (IRI-perspective taking subscale; rho = 0.471,
The aim of this study was to test for differences in the regulation of emotions stemming from interaction with human and non-human partners respectively. Results indicated that even though reappraisal can be successfully applied to both contexts, participants showed a stronger effect on their perceived valence when playing with a human partner. Therefore, it seems that reappraisal leads participants to change the valence of their emotions to make them more positive for selfish offers, but also stronger and more vivid for fair offers. Moreover, emotional ratings indicated that on one hand, participants were more disappointed and disgusted when recipients of selfish behavior from human rather than computer partners, however when receiving altruistic offers participants were happier when the Allocator was a human partner.
Last but not least, there was a positive correlation between IRI and ERQ questionnaires, indicating that the ability to take the psychological point of view of others and emotion regulation abilities are related. Indeed, the IRI (perspective taking subscale) addresses one’s tendency to take another’s point of view, akin to “theory of mind” (Davis,
Our ability to regulate emotions when interacting with others is considered to be a crucial dimension of both emotional intelligence (Mayer and Salovey,
Firstly, our data demonstrate that emotion regulation can be successfully applied to socially driven emotions. Across both experiments participants reported an increase in valence (that is, less unpleasant emotions) when reappraising the intentions behind both selfish and altruistic behavior. More importantly, Experiment 1 showed that not all emotion regulation strategies are equally good at altering our emotional responses. While mentalizing-based reappraisal (defined as the “
Experiment 2 tested whether reappraisal can also be used when the emotion elicited comes from a non-human partner. This is important to appreciate differences in emotional regulation when applied to social and non-social contexts. Even though both conditions showed a modulation of emotional valence when receiving selfish proposals, there was a difference of partner type. Valence change was stronger when participants regulated their emotions in response to human offers. In fact, when comparing human and computer in the baseline condition, this difference was doubled in the reappraisal condition. Arousal analyses showed interesting differences in increasing the strength of vividness of experienced emotions when they were associated with an altruistic behavior.
Both experiments showed interesting results regarding the perception of the strength of the emotional experience, i.e., arousal. When using reappraisal based on cognitive reinterpretation, both experiments showed that once unpleasant (and at a lesser extent also positive) emotions are changed in terms of their valence (perceived as less unpleasant) arousal is increased (evident for €5 offer in experiment 2), meaning that emotion regulation strategies that are effective in reframing the events in a more positive way let us experience our emotions more vividly. In contrast, Experiment 1 showed that distancing-based reappraisal did not change the experienced emotion (unpleasant emotions in response to selfish offers are still perceived as unpleasant, and pleasant emotions in response to fair offers are even less pleasant). One conclusion is therefore that not all strategies are effective to the same extent in regulating our emotions. Even though distancing may mitigate individuals’ experience of their emotions by avoiding them, in the long run it can lead individuals to progressively detach from others and from situations. This in turn may lead to anhedonia and isolation as shown by many psychiatric disorders (Leising et al.,
Psychological studies have shown that cognitive reappraisal is one of the most flexible and adaptive strategies for regulating negative emotions (Gross,
The present experiment also extends previous findings on decision-making. Broadly speaking, emotion regulation strategies applied to decision-making have one notable advantage as compared to basic emotion regulation studies: they have the opportunity to study complex emotions that cannot be elicited in simple visual stimuli tasks. Emotions elicited by the outcome of our decisions are of a qualitatively different nature than those experienced while simply watching disturbing images, and so it was an open question whether these strategies can be effective in regulating such emotions and influencing decision behavior in real-life. In everyday life we are typically confronted with a variety of emotions directly induced by decisions, by the evaluation of risks and possible losses, and last but not least by social interactions, and emotion regulation seems particularly useful in such contexts. Therefore, investigating whether emotion regulation strategies can have an effect in decision-making contexts has the opportunity to extend emotion regulation research beyond affective responses to simple emotional pictures into more complex scenarios. Social norms, such as fairness, equality, and cooperation, play a fundamental role in societies (Deutsch,
On the same line, when reappraising, the identity of the player matters: we are more prone to “excuse” the selfishness of a human rather than a non-human donor. The justification of occasional violations of social norms may be functional in keeping cooperation high between individuals belonging to the same group.
In recent years, progress in understanding the neural mechanisms of emotional regulation has used functional imaging to identify the neural signatures of regulation (Ochsner and Gross,
In conclusion, we investigated the effect of reappraisal based emotion regulation strategies, and further looked at the effects of playing with a human or a non-human (computer) partner. We believe these results are important as they shed light on two points: the possibility of regulating socially driven emotions on one hand, and the effect of different strategies themselves on the other. Our results show that emotional reappraisal specifically influences emotions stemming from the interaction with altruistic and selfish proposers. Both emotions elicited by altruistic and selfish offers showed an effect of regulation for the two main dimensions of emotional experience: valence and arousal. These results extend previous findings on this topic and hold the promise of shedding light on the understanding of interpersonal problems shown by psychiatric populations due to poor emotion regulation (Werner and Gross,
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.
The way we perceive an event may alter the perception of the event in a way to make it more or less negative.
See for example the following image:
One interpretation can be that this woman is suffering because of the death of a beloved one. Another interpretation is that she is simply tired. Both are plausible interpretations, but the effect of these instructions may be different. The first increases the perceived negativity of the event, the second decreases it. We are asking you to make an effort of reinterpretation of the event in a way to decrease its negativity…
Can you generate another example of how to reinterpret that picture as less negative?…
Now we will teach you how to apply this strategy to the domain of the Dictator Game.
In the following part of the experiment you are asked to reinterpret the intentions of your partner in a way to consider them as less negative…
Subjects were given some examples on how to apply this strategy to DG:
“You can think that this person has no money to give you,” “He/she is in troubles,” “In another situation he/she may be more generous”
Another useful strategy that people can use to decrease the negativity of an event, is to take the distance from it.
See for example the following image:
Such a situation is undoubtedly unpleasant. However, the fact that we are more or less involved in this situation determines how negative we perceive that situation. Someone can think that this situation has great relevance for himself/herself and perceive it as very negative. Someone else may in turn think it does not affect his/her life.
These two ways of thinking, in touch or detached from the situation, alter the way we perceive that situation…
Can you generate another example of how to think in a detached way that picture?…
Now we will teach you how to apply this strategy to the domain of the Dictator Game.
When asked to apply such a strategy you should put yourself in a detached perspective and think that this situation is not relevant for you.
Subjects were then given some examples on how to apply this strategy to DG:
“This offer won’t affect your economic situation,” “I don’t care of your money,” “I don’t even know you”
Look at the offers and make your response in a spontaneous way without applying any strategy.
This research was supported by a Provincia Autonoma di Trento (PAT) Researcher Grant to Alan G. Sanfey. We would like to thank Sara Lorandini for her help during the acquisition of the data.