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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.
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.