An Exploratory Study of Loss Averse in Group Decision Contexts: Multiple Pieces of Evidence From ERPs and Machine Learning
Jia Ke Jin, Zhongfeng Wang, Lu Dai, A. Ting Wang, Li Ting Gao · Psychophysiology · 2025
Both laboratory and field evidence have shown differences in risk attitudes between individual and group decision contexts. Loss aversion, a crucial aspect of risk attitudes, whose behavioral performance and neural mechanism in group decision contexts remain unclear, differs from other risk attitudes such as risk aversion. Using behavioral and electroencephalography (EEG) experiments with non-student and student samples, we conducted an exploratory study to examine the behavioral performance and neural mechanisms of loss aversion in group decision contexts. Behaviorally, we found a reduction effect of loss aversion in group decision contexts compared to individual decision contexts. ERP results from the average and single-trial analyses jointly found that individuals are less sensitive to losses and gains in group (vs. individual) decision contexts, as evidenced by the vanishing Feedback-related Negativity (FRN) and P3b differences to losses and gains. We also found a significant negative correlation between the loss aversion coefficient and FRN amplitude induced by losses both in individual and group decision contexts, which indicated the relationship between loss aversion and neural signals that process loss outcomes. Furthermore, machine learning analyses revealed that EEG features exhibit a high accuracy rate of 81.25% in predicting the decision contexts. This finding underscores the intricate relationship between neural activity and loss aversion across varying decision contexts, highlighting the potential of neurophysiological activity to elucidate the underlying cognitive processes involved in loss aversion. This paper advances our understanding of loss aversion in group decision contexts by providing multiple pieces of evidence for behavioral performance, neural activities, and machine learning. Findings can help to optimize group decision-making processes and resource allocation, and to reduce inefficiencies caused by irrational behavior and resistance to beneficial changes.