Department of Cognitive Science, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.
Department of Material Science & Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.
Proc Natl Acad Sci U S A. 2024 May 21;121(21):e2313801121. doi: 10.1073/pnas.2313801121. Epub 2024 May 16.
Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks suboptimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives, or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus-building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy, and group cohesion, in 44 groups (n = 204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multidimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with >70% cross-validation accuracy-significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.
群体在解决问题方面通常优于个体。然而,如果不能批判性地评估想法,就有可能通过所谓的群体思维导致次优结果。先前的研究表明,持有共同目标、观点或对环境的理解的人表现出相似的大脑活动模式,而通过达成共识的讨论可以增强这种模式。然而,共享兴奋感本身是否可以预测集体决策结果仍然未知。为了解决这一差距,我们在 44 个小组(n = 204)中计算了人际心率同步性,这是与共同注意力、共情准确性和群体凝聚力相关的共享兴奋的外周指标,这些小组正在进行集体决策任务。该任务要求批判性地检查所有可用信息,以克服较差的默认选项并做出正确的选择。使用多维递归定量分析(MdRQA)和机器学习,我们发现心率同步性可以预测小组达到正确共识决策的概率,其交叉验证准确率超过 70%,明显高于讨论时间、团队功能的主观评估或基线心率单独预测的准确率。我们提出,小组讨论期间的心率同步性提供了人际参与的生物标志物,有助于在集体决策过程中进行适应性学习和有效信息共享。