how non-verbalcommunication changes when participants are engaged while interacting in a videoconference call and while interacting in person

在过去的十年里，视频会议技术极大地改变了人们在工作场所和个人生活中的社交互动。新冠肺炎的流行进一步迫使这种变化到了远程工作被广泛采用的地步。在这种情况下，理解人们在这种情况下如何调整他们的行为以进行有效的沟通是极其重要的。人类面对面的社交不仅仅建立在语言交流的基础上，还受到非语言社交信号(如眼睛凝视、面部表情等)的严重影响。这对于有效的沟通是必不可少的。在此背景下，本项目将研究当参与者在视频电话会议中互动(使用ZOOM平台)和当面互动时，非语言沟通是如何变化的。在这两种情况下，参与者将执行两项任务：导演任务和心理旋转任务。在整个实验过程中，参与者将使用一种名为fNIRS的神经成像技术在超扫描环境中(即同时从两个或更多参与者那里获取大脑信号)来测量大脑活动。我们还将记录眼球运动和面部运动。首先，我们将分析非语言信号，眼睛凝视和面部动作，分别针对两个任务和每个参与者。然后，我们将使用最近制定的“xGLM”模型来分析跨脑同步性，该模型根据参与者的行为、生理数据和伴侣的大脑活动、行为和生理数据来模拟参与者的大脑活动。我们项目的结果将是：首先，我们的实验与高级分析相结合的高生态环境将使我们能够理解在完成任务时视频通话和面对面互动之间的社交信号是如何不同的，并检查这种共享经验招募了哪些大脑系统。其次，通过了解这些互动背后的机制，我们将能够为更好的在线沟通制定新的实用指导方针。

In the past decade, videoconference technology has drastically changed people's social interaction both in the work place and in personal life. The Covid-19 pandemic further forced this change to a point where remote-work is widely adopted. In this context, understanding how people adapt their behaviour in this situation to effectively communicate is extremely important. Human face-to-face social interactions are not based only on verbal communication, but they are also heavily influenced by non-verbal social signals (e.g. eye gaze, facial expression etc.) which are essential for effective communication. In this context, this project will study how non-verbal communication changes when participants are engaged while interacting in a videoconference call (using zoom platform) and while interacting in-person. In both situations, participants will perform two tasks: the director task and the mental rotation task. Participants brain activity will be measured using a neuroimaging technique called fNIRS in a hyper-scanning settings (i.e. acquire simultaneously the brain signal from two or more participants) throughout the experiment. We will also record eye movements and facial motions. First, we will analyse the non-verbal signal,eye gaze and facial motions, separately for the two tasks and for each participant. Then, we will analyse cross-brain synchrony using the recently formulated "xGLM" model, which models the brain activity of a participant in terms of her/him behaviour, physiological data and the partner's brain activity, behaviour and physiological data. The outcome of our project will be: first, the high ecological settings of our experiment in conjunction with advanced analysis will allow us to understand how social signals are different between videocall and in-person interaction while completing tasks, and to examine which brain systems are recruited by this shared experience. Second, fromunderstanding the mechanism behind these interactions, we will be able to develop new practical guidelines forbetter online communication.