Affective facial expression processing via simulation: a probabilistic model

Understanding the mental state of other people is an important skill for intelligent agents and robots to operate within social environments. However, the mental processes involved in ‘mind-reading’ are complex. One explanation of such processes is Simulation Theory—it is supported by a large body of neuropsychological research. Yet, determining the best computational model or theory to use in simulation-style emotion detection, is far from being understood. In this work, we use Simulation Theory and neuroscience findings on Mirror-Neuron Systems as the basis for a novel computational model, as a way to handle affective facial expressions. The model is based on a probabilistic mapping of observations from multiple identities onto a single fixed identity (‘internal transcoding of external stimuli’), and then onto a latent space (‘phenomenological response’). Together with the proposed architecture we present some promising preliminary results.