When people interact, their behavior tends to become synchronized, a mutual coordination process that fosters short-term adaptations, like increased affiliation, and long-term adaptations, like increased bonding. This paper addresses for the first time how such short-term and long-term adaptivity induced by synchronization can be modeled computationally by a second-order multi-adaptive neural agent model. It addresses movement, affect and verbal modalities and both intrapersonal synchrony and interpersonal synchrony. The behavior of the introduced neural agent model was evaluated in a simulation paradigm with different stimuli and communication-enabling conditions. Moreover, in this paper, mathematical analysis is also addressed for adaptive network models and their positioning within the landscape of adaptive dynamical systems. The first type of analysis addressed shows that any smooth adaptive dynamical system has a canonical representation by a self-modeling network. This implies theoretically that the self-modeling network format is widely applicable, which also has been found in many practical applications using this approach. Furthermore, stationary point and equilibrium analysis was addressed and applied to the introduced self-modeling network model. It was used to obtain verification of the model providing evidence that the implemented model is correct with respect to its design specifications.

当人们互动时，他们的行为往往会变得同步，这是一个相互协调的过程，可以促进短期适应（例如增加归属感）和长期适应（例如增加联系）。本文首次讨论了如何通过二阶多自适应神经代理模型对同步引起的短期和长期适应性进行计算建模。它涉及运动、情感和言语方式以及个人内部同步和人际同步。所引入的神经代理模型的行为在具有不同刺激和通信支持条件的模拟范例中进行了评估。此外，本文还针对自适应网络模型及其在自适应动态系统领域中的定位进行了数学分析。第一种类型的分析表明，任何平滑自适应动力系统都具有自建模网络的规范表示。这在理论上意味着自建模网络格式具有广泛的适用性，并且在使用这种方法的许多实际应用中也发现了这一点。此外，还解决了驻点和平衡分析并将其应用于引入的自建模网络模型。它用于获得模型的验证，提供证据证明所实施的模型在其设计规范方面是正确的。