Abstract
Differences in social responses of individuals can often be related to differences in functioning of certain neurological mechanisms. Based on a Network-Oriented Modeling approach, a temporal-causal network model has been developed that is capable of showing different types of social response patterns according to such mechanisms, adopted from theories on mirror neuron systems, emotion integration, emotion regulation, and empathy. Within the model also adaptive capabilities have been incorporated, showing how learning of social response patterns can take place. The adaptive temporal-causal network model provides a basis for human-like social response patterns of virtual agents in the context of simulation-based training (e.g., for training of physicians or therapists), gaming, or for generation of virtual stories.
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