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

• URL: http://arxiv.org/abs/2006.11465v2
• Date: Sun, 12 Jul 2020 04:30:56 GMT
• Title: Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives
• Authors: Junpei Zhong and Angelo Cangelosi and Stefan Wermter
• Abstract summary: The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. We exemplify this model through a robot passively observing an object to learn its features and movements.
• Score: 15.364871660385155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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