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Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives ...
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Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs ...
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Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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Abstract:
Copyright ©2014 Zhong, Cangelosi and Wermter.This is an open-access article distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms ; 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. ; Peer reviewed ; Final Published version
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Keyword:
Behavioral Neuroscience; Cognitive Neuroscience; Horizontal product; Neuropsychology and Physiological Psychology; Parametric biases; Pre-symbolic communication; Recurrent neural networks; Sensorimotor integration
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URL: http://hdl.handle.net/2299/14543
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Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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