Discovering the antecedents of individuals' influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We observe that along an issue sequence with feedback, individuals with higher expertise and social confidence are accorded higher interpersonal influence. We also observe that low-performing individuals tend to underestimate their high-performing teammate's expertise. Based on these observations, we introduce three hypotheses and present empirical and theoretical support for their validity. We report empirical evidence on longstanding theories of transactive memory systems, social comparison, and confidence heuristics on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time. We demonstrate the model's accuracy in predicting individuals' influence and provide analytical results on its asymptotic behavior for the case with identically performing individuals. Lastly, we propose a novel approach using deep neural networks on a pre-trained text embedding model for predicting the influence of individuals. Using message contents, message times, and individual correctness collected during tasks, we are able to accurately predict individuals' self-reported influence over time. Extensive experiments verify the accuracy of the proposed models compared to baselines such as structural balance and reflected appraisal model. While the neural networks model is the most accurate, the dynamical model is the most interpretable for influence prediction.

中文翻译：

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专业知识和信心解释了社会影响如何随着智力任务而演变

在协作环境中发现个人影响的前因是一个重要的、实际的和具有挑战性的问题。在本文中，我们研究了集体执行一系列智力任务的小团体的人际影响。我们观察到，沿着带有反馈的问题序列，具有更高专业知识和社会信心的个人被赋予更高的人际影响。我们还观察到，表现不佳的人往往会低估他们表现出色的队友的专业知识。基于这些观察，我们引入了三个假设，并为它们的有效性提供了经验和理论支持。我们报告了有关交互记忆系统、社会比较和关于社会影响起源的信心启发法的长期理论的经验证据。我们提出了一个受这些理论启发的认知动力学模型，以描述个人随时间调整人际关系影响的过程。我们证明了该模型在预测个人影响方面的准确性，并为具有相同表现的个人的案例提供了其渐近行为的分析结果。最后，我们提出了一种在预训练文本嵌入模型上使用深度神经网络来预测个人影响的新方法。使用在任务期间收集的消息内容、消息时间和个人正确性，我们能够准确预测个人自我报告的影响随时间的推移。与结构平衡和反射评估模型等基线相比，大量实验验证了所提出模型的准确性。