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Previous EEG research only investigated one stage ultimatum games (UGs). We investigated the influence of a second bargaining stage in an UG concerning behavioral responses, electro-cortical correlates and their moderations by the traits altruism, anger, anxiety, and greed in 92 participants. We found that an additional stage led to more rejection in the 2-stage UG (2SUG) and that increasing offers in the second stage compared to the first stage led to more acceptance. The FRN during a trial was linked to expectance evaluation concerning the fairness of the offers, while midfrontal theta was a marker for the needed cognitive control to overcome the respective default behavioral pattern. The FRN responses to unfair offers were more negative for either low or high altruism in the UG, while high trait anxiety led to more negative FRN responses in the first stage of 2SUG, indicating higher sensitivity to unfairness. Accordingly, the mean FRN response, representing the trait-like general electrocortical reactivity to unfairness, predicted rejection in the first stage of 2SUG. Additionally, we found that high trait anger led to more rejections for unfair offer in 2SUG in general, while trait altruism led to more rejection of unimproving unfair offers in the second stage of 2SUG. In contrast, trait anxiety led to more acceptance in the second stage of 2SUG, while trait greed even led to more acceptance if the offer was worse than in the stage before. These findings suggest, that 2SUG creates a trait activation situation compared to the UG.
There continues to be difficulties when it comes to replication of studies in the field of Psychology. In part, this may be caused by insufficiently standardized analysis methods that may be subject to state dependent variations in performance. In this work, we show how to easily adapt the two-layer feedforward neural network architecture provided by Huang1 to a behavioral classification problem as well as a physiological classification problem which would not be solvable in a standardized way using classical regression or “simple rule” approaches. In addition, we provide an example for a new research paradigm along with this standardized analysis method. This paradigm as well as the analysis method can be adjusted to any necessary modification or applied to other paradigms or research questions. Hence, we wanted to show that two-layer feedforward neural networks can be used to increase standardization as well as replicability and illustrate this with examples based on a virtual T-maze paradigm\(^{2−5}\) including free virtual movement via joystick and advanced physiological data signal processing.
In human interactions, the facial expression of a bargaining partner may contain relevant information that affects prosocial decisions. We were interested in whether facial expressions of the recipient in the dictator game influence dictators´ ehavior. To test this, we conducted an online study (n = 106) based on a modified version of a dictator game. The dictators allocated money between themselves and another person (recipient), who had no possibility to respond to the dictator.
Importantly, before the allocation decision, the dictator was presented with the facial expression of the recipient (angry, disgusted, sad, smiling, or neutral). The results showed that dictators sent more money to recipients with sad or smiling facial expressions and less to recipients with angry or disgusted facial expressions compared with a neutral facial expression. Moreover, based on the sequential analysis of the decision and the interaction partner in the preceding trial, we found that decision-making depends upon previous interactions.
In everyday life, assumptions about our peers' as well as our own personality shape social interactions. We investigated whether self-rated personality and inferences drawn from partners' faces influence economic decisions. Participants (N = 285) played the trust game in the role of the trustor as well as the ultimatum game in the role of the proposer and interacted with trustees and receivers represented by prototypical personality faces. Participants also evaluated both their own traits and the personality of the faces. In the trust game, trustees represented by faces rated higher on agreeableness yielded higher transferred amounts. This effect was more pronounced for trustors low on dispositional trust, whereas trustors high on dispositional trust did not relate their decisions to the faces. Trustees represented by faces rated higher on conscientiousness yielded higher transferred amounts only for trustors high on dispositional anxiety. In the ultimatum game, receivers represented by faces rated higher on conscientiousness yielded lower offers only for proposers high on dispositional assertiveness. These results extend previous findings on the inferences drawn from facial features and the influence of personality on decision making. They highlight the importance of considering the personality of both interaction partner, as well as potential interactions of players' traits.
To slow down the spread of the SARS-Cov-2 virus, countries worldwide severely restricted public and social life. In addition to the physical threat posed by the viral disease (COVID-19), the pandemic also has implications for psychological well-being. Using a small sample (N = 51), we examined how Big Five personality traits relate to coping with contact restrictions during three consecutive weeks in the first wave of the COVID-19 pandemic in Germany. We showed that extraversion was associated with suffering from severe contact restrictions and with benefiting from their relaxation. Individuals with high neuroticism did not show a change in their relatively poor coping with the restrictions over time, whereas conscientious individuals seemed to experience no discomfort and even positive feelings during the period of contact restrictions. Our results support the assumption that neuroticism is a vulnerability factor in relation to psychological wellbeing but also show an influence of contact restrictions on extraverted individuals.
We investigated the influence of social status on behavior in a modified dictator game (DG). Since the DG contains an inherent dominance gradient, we examined the relationship between dictator decisions and recipient status, which was operationalized by three social identities and an artificial intelligence (AI). Additionally, we examined the predictive value of social dominance orientation (SDO) on the behavior of dictators toward the different social and non-social hierarchical recipients. A multilevel model analysis showed that recipients with the same status as the dictator benefited the most and the artificial intelligence the least. Furthermore, SDO, regardless of social status, predicted behavior toward recipients in such a way that higher dominance was associated with lower dictator offers. In summary, participants treated other persons of higher and lower status equally, those of equal status better and, above all, an algorithm worst. The large proportion of female participants and the limited variance of SDO should be taken into account with regard to the results of individual differences in SDO.