This study examines the impact of feedback on Electroencephalography (EEG) activity and performance during the Reading the Mind in the Eyes Test. In a within-subject design, eleven participants completed the test under Feedback and No-Feedback conditions. Using the principles of Epistemic Network Analysis (ENA) and Ordered Network Analysis (ONA), we extend these network-based models to explore the link between neural dynamics and task outcomes. ENA results showed that feedback is associated with stronger connections between higher frequency EEG bands (Beta and Gamma) and correct responses, while the absence of feedback activated lower frequency bands (Theta and Alpha). ONA further disclosed directional shifts toward higher frequency activity preceding correct answers in the Feedback condition, whereas the No-Feedback condition showed more self-connections in lower bands and a higher occurrence of wrong answers, suggesting less effective reasoning strategies without feedback. Both ENA and ONA revealed statistically significant differences between conditions (p = 0.01, Cohen's d > 2). This study highlights the methodological benefits of integrating EEG with ENA and ONA for network analysis, capturing both temporal and relational dynamics, as well as the practical insight that feedback can foster more effective reasoning processes and improve task performance.

翻译：本研究探讨了在"眼中心智解读测试"中反馈对脑电图活动与行为表现的影响。采用被试内设计，11名参与者在有反馈和无反馈条件下完成测试。基于认知网络分析与有序网络分析原理，我们将这些基于网络的模型拓展用于探究神经动态与任务结果之间的关联。认知网络分析结果显示，反馈与高频脑电节律和正确反应之间更强的连接相关，而无反馈条件则激活了低频节律。有序网络分析进一步揭示了反馈条件下正确回答前存在向高频活动的方向性转变，而无反馈条件则表现出更多低频节律的自连接及更高的错误回答发生率，这表明缺乏反馈时推理策略效率较低。两种分析方法均显示条件间存在统计学显著差异。本研究凸显了将脑电图与认知网络分析/有序网络分析相结合进行网络分析的方法学优势，能够同时捕捉时间动态与关系动态；同时提供了实践启示：反馈能够促进更有效的推理过程并提升任务表现。