  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.
