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Neural mechanisms of altruistic decision-making: EEG functional connectivity network analysisстатья Исследовательская статья
- Авторы: Mitiureva Dina G., Terlichenko Evgenia O., Zubko Veronika M., Kabanova Polina I., Abrosimova Vasilisa D., Skripkina Sofya M., Krivchenkova Elizaveta V., Verkholaz Daria M., Borodkina Anna S., Komarova Alisa V., Kiselnikov Andrey A.
- Журнал: Cognitive, Affective and Behavioral Neuroscience
- Том: 24
- Год издания: 2024
- Издательство: Psychonomic Society Inc.
- Местоположение издательства: United States
- Первая страница: 1109
- Последняя страница: 1120
- DOI: 10.3758/s13415-024-01214-8
- Аннотация: Altruism is an enigmatic form of prosocial behavior, characterized by diverse motivations and significant interindividual differences. Studying neural mechanisms of altruism is crucial to identify objective markers of pro- and antisocial tendencies in behavior. This study was designed to delve into the mechanisms of altruism by analyzing EEG-based functional connectivity patterns within the framework of the network approach. To experimentally induce a situation of altruistic decision-making, we employed the Pain versus Gain (PvsG) task, which implies making choices concerning financial self-benefit and pain of the other. Our results reveal that the behavioral measure of altruism in the experiment correlated with emotional empathy, which is in line with the “empathy-altruism” hypothesis. Applying the network approach to EEG functional connectivity analysis, we discovered that the very process of decision-making in the PvsG is characterized by the synchronous activity of structures in the right hemisphere, which are involved in empathy for pain. The prosociality of decisions was reflected in functional connectivity between the rostral ACC and orbital IFG in the left hemisphere and the overall network centrality of the caudal ACC. This finding additionally points to the distinct functional roles of the ACC subregions in altruistic decision-making. The proposed neural mechanisms of altruism can further be used to identify neurophysiological markers of prosociality in behavior.
- Добавил в систему: Кисельников Андрей Александрович