Touch and emotion: Modeling of developmental differentiation of emotion lead by tactile dominance

Emotion is one of the important elements for humans to communicate with others. Humans are known to share basic emotions such as joy and anger although their developmental changes have been studied less. We propose a computational model for the emotional development in infancy. Our model reproduces the differentiation of emotion from pleasant/unpleasant states to six basic emotions as known in psychological studies. The key idea is twofold: the tactile dominance in infant-caregiver interaction and the inherent ability of tactile sense to discriminate pleasant/unpleasant states. Our model consists of probabilistic neural networks called Restricted Boltzmann Machines. The networks are hierarchically organized to first extract important features from tactile, auditory, and visual stimuli and then to integrate them to represent an emotional state. Pleasant/unpleasant information is directly provided to the highest level of the network to facilitate the emotional differentiation. Experimental results show that our model with the tactile dominance leads to better differentiation of emotion than others without such dominance.