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

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 se...

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Main Authors:	Zhong, Junpei, Cangelosi, Angelo, Wermter, Stefan
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912404/

id	pubmed-3912404
recordtype	oai_dc
spelling	pubmed-39124042014-02-18 Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives Zhong, Junpei Cangelosi, Angelo Wermter, Stefan Neuroscience 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. Frontiers Media S.A. 2014-02-04 /pmc/articles/PMC3912404/ /pubmed/24550798 http://dx.doi.org/10.3389/fnbeh.2014.00022 Text en Copyright © 2014 Zhong, Cangelosi and Wermter. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zhong, Junpei Cangelosi, Angelo Wermter, Stefan
spellingShingle	Zhong, Junpei Cangelosi, Angelo Wermter, Stefan Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
author_facet	Zhong, Junpei Cangelosi, Angelo Wermter, Stefan
author_sort	Zhong, Junpei
title	Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
title_short	Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
title_full	Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
title_fullStr	Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
title_full_unstemmed	Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
title_sort	toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
description	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.
publisher	Frontiers Media S.A.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912404/
_version_	1612054264978341888

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

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