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.
DOI
10.3389/fnbeh.2014.00022
Publication Date
2014-01-01
Publication Title
Frontiers in Behavioral Neuroscience
Volume
8
Publisher
Frontiers Media SA
ISSN
1662-5153
Embargo Period
2024-11-22
Recommended Citation
Zhong, J., Cangelosi, A., & Wermter, S. (2014) 'Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives', Frontiers in Behavioral Neuroscience, 8. Frontiers Media SA: Available at: https://doi.org/10.3389/fnbeh.2014.00022
