专业知识与自信心解释了社会影响力在智力任务中如何演变
Expertise and confidence explain how social influence evolves along intellective tasks
AI总结：
本文研究了小群体执行智力任务时人际影响力的演变，发现专业知识与社交自信更高的个体影响力更大，并提出了认知动力学模型和深度神经网络预测方法，兼顾了预测准确性与可解释性。
摘要：
发现在协作环境中个体影响力的前因是一个重要、实用且具挑战性的问题。本文研究了在共同执行一系列智力任务的小群体中的人际影响力。我们观察到，在带有反馈的问题序列中，具有较高专业知识和社交自信的个体会被赋予更高的人际影响力。我们还观察到，表现不佳的个体往往会低估其表现优异的队友的专业知识。基于这些观察，我们提出了三个假设，并为其有效性提供了实证和理论支持。我们报告了关于交互记忆系统、社会比较和置信度启发式等长期理论在社会影响力起源方面的实证证据。我们提出了一个受这些理论启发的认知动力学模型，以描述个体随时间调整人际影响力的过程。我们展示了该模型在预测个体影响力方面的准确性，并针对表现相同的个体情况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的新方法，用于预测个体的影响力。利用任务期间收集的消息内容、消息时间和个体正确性，我们能够准确预测个体随时间变化的自述影响力。大量实验验证了所提出模型与结构平衡和反映评估模型等基线相比的准确性。虽然神经网络模型最准确，但动力学模型在影响力预测方面最具可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
