Abstract In this paper an adaptive agent model is presented with capabilities to interpret another agent’s emotions. The presented agent model is based on recent advances in neurological context. First a non-adaptive agent model for emotion reading is described involving (preparatory) mirroring body states of the other agent. Here emotion reading is modelled taking into account the Simulation Theory perspective as known from the literature, involving the own body states and emotions in reading somebody else’s emotions. This models an agent that first develops the same feeling, and after feeling the emotion imputes it to the other agent. Next the agent model is extended to an adaptive model based on a Hebbian learning principle to develop a direct connection between a sensed stimulus concerning another agent’s body state (e.g., face expression) and the emotion recognition state. In this adaptive agent model the emotion is imputed to the other agent before it is actually felt. The agent model has been designed based on principles of neural modelling, and as such has a close relation to a neurological realisation.

This paper addresses the use of Hebbian learning principles to model in an adaptive manner capabilities to interpret somebody else’s emotions. First a non-adaptive neural model for emotion reading is described involving (preparatory) mirror neurons and a recursive body loop: a converging positive feedback loop based on reciprocal causation between mirror neuron activations and neuron activations underlying emotions felt. Thus emotion reading is modelled taking into account the Simulation Theory perspective as known from the literature, involving the own emotions in reading somebody else’s emotions. Next the neural model is extended to an adaptive neural model based on Hebbian learning within which a direct connection between a sensed stimulus concerning another person’s body state (e.g., face expression) and the emotion recognition state is strengthened.

Abstract—In the mirror neuron system, the canonical neurons play a role in object shape and observer-object relation recognition. However, there are almost no functional models of canonical neurons towards the integration of these two functions. We attempt to represent the relative position between the object and the robot in a neural network model. Although at present some generative models based on the Restricted Boltzmann Machine can code the image transformation in continuous images, what we need to accomplish in canonical neuron modeling is different from the requirements of modeling transformation in video frames. As a result, we propose a novel model called “Restricted Boltzmann Machine with Transformation Units”, which can represent the relative object positions based on laser images. The laser sensor provides binary and accurate images and can further be connected with other models to construct a unified architecture of the mirror neuron system. I.