Maniezzo, Vittorio ; Roffilli, Matteo
(2005)
A Psychogenetic Algorithm for Behavioral Sequence Learning.
[Preprint]
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Abstract
This work presents an original algorithmic model of some essential features of psychogenetic theory, as was proposed by J.Piaget. Specifically, we modeled some elements of cognitive structure learning in children from 0 to 4 months of life. We are in fact convinced that the study of well-established cognitive models of human learning can suggest new, interesting approaches to problem so far not satisfactorily solved in the field of machine learning. Further, we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience. The model was implemented and tested in some simple experimental settings, with reference to the task of learning sensorimotor sequences.
Abstract
This work presents an original algorithmic model of some essential features of psychogenetic theory, as was proposed by J.Piaget. Specifically, we modeled some elements of cognitive structure learning in children from 0 to 4 months of life. We are in fact convinced that the study of well-established cognitive models of human learning can suggest new, interesting approaches to problem so far not satisfactorily solved in the field of machine learning. Further, we discussed the possible parallels between our model and subsymbolic machine learning and neuroscience. The model was implemented and tested in some simple experimental settings, with reference to the task of learning sensorimotor sequences.
Document type
Preprint
Creators
Keywords
Psychogenetic theory, behavior sequence learning, machine learning, symbolic learning, genetic epistemology, J.Piaget
Subjects
DOI
Deposit date
06 Jun 2005
Last modified
06 May 2015 07:51
URI
Other metadata
Document type
Preprint
Creators
Keywords
Psychogenetic theory, behavior sequence learning, machine learning, symbolic learning, genetic epistemology, J.Piaget
Subjects
DOI
Deposit date
06 Jun 2005
Last modified
06 May 2015 07:51
URI
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