Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives

• Main Authors: Junpei eZhong, Angelo eCangelosi, Stefan eWermter
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2014-02-01
• Series: Frontiers in Behavioral Neuroscience
• Subjects: Forward models; recurrent neural networks; sensorimotor integration; Pre-symbolic Communication; Parametric Biases
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnbeh.2014.00022/full

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e. observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.