title: Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Hamburg Hamburg, Germany ; School of Computer Science, University of Hertfordshire Hatfield, UK.
authors.forename: Junpei
authors.initials: J
authors.lastname: Zhong
authors.affiliation: School of Computing and Mathematics, University of Plymouth Plymouth, UK.
authors.forename: Angelo
authors.initials: A
authors.lastname: Cangelosi
authors.affiliation: Department of Computer Science, University of Hamburg Hamburg, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Wermter
keywords: horizontal product; parametric biases; pre-symbolic communication; recurrent neural networks; sensorimotor integration