Towards a Self-organizing Pre-symbolic Neural Model Representing Sensorimotor Primitives

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.

@Article{ZCW14,
 	 author =  {Zhong, Junpei and Cangelosi, Angelo and Wermter, Stefan},
 	 title = {Towards a Self-organizing Pre-symbolic Neural Model Representing Sensorimotor Primitives},
 	 booktitle = {None},
 	 journal = {Frontiers in Behavioral Neuroscience},
 	 editors = {None},
 	 number = {22},
 	 volume = {8},
 	 pages = {1--11},
 	 year = {2014},
 	 month = {Feb},
 	 publisher = {Frontiers Media S.A.},
 	 doi = {10.3389/fnbeh.2014.00022},
 }