A Neural Model for Adaptive Emotion Reading Based on Mirror Neurons and Hebbian Learning


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.

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