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Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition

A pivotal tenet in modern behavioral sciences is that the study of behavior, in its most intimate structure, necessarily deals with time and, for this reason, behavioral dynamics are not intuitively perceivable and/or detectable (Eibl-Eibesfeldt, 1970). In reality, the possibility to describe a give...

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Autor principal: Casarrubea, Maurizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694199/
https://www.ncbi.nlm.nih.gov/pubmed/33158104
http://dx.doi.org/10.3390/brainsci10110818
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author Casarrubea, Maurizio
author_facet Casarrubea, Maurizio
author_sort Casarrubea, Maurizio
collection PubMed
description A pivotal tenet in modern behavioral sciences is that the study of behavior, in its most intimate structure, necessarily deals with time and, for this reason, behavioral dynamics are not intuitively perceivable and/or detectable (Eibl-Eibesfeldt, 1970). In reality, the possibility to describe a given behavior in terms of its structural/temporal features makes available new and detailed information otherwise unavailable. The aim of the present paper is to discuss the possible application of T-pattern detection and analysis, i.e., a multivariate approach specifically developed to describe the temporal structure of behavior, to the study of an important and still scantly investigated issue, namely the detection of behavioral correlates of the neurophysiological phenomenon known as afferent inhibition.
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spelling pubmed-76941992020-11-28 Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition Casarrubea, Maurizio Brain Sci Editorial A pivotal tenet in modern behavioral sciences is that the study of behavior, in its most intimate structure, necessarily deals with time and, for this reason, behavioral dynamics are not intuitively perceivable and/or detectable (Eibl-Eibesfeldt, 1970). In reality, the possibility to describe a given behavior in terms of its structural/temporal features makes available new and detailed information otherwise unavailable. The aim of the present paper is to discuss the possible application of T-pattern detection and analysis, i.e., a multivariate approach specifically developed to describe the temporal structure of behavior, to the study of an important and still scantly investigated issue, namely the detection of behavioral correlates of the neurophysiological phenomenon known as afferent inhibition. MDPI 2020-11-04 /pmc/articles/PMC7694199/ /pubmed/33158104 http://dx.doi.org/10.3390/brainsci10110818 Text en © 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Editorial
Casarrubea, Maurizio
Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition
title Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition
title_full Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition
title_fullStr Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition
title_full_unstemmed Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition
title_short Possible Contribution of T-pattern Detection and Analysis to the Study of the Behavioral Correlates of Afferent Inhibition
title_sort possible contribution of t-pattern detection and analysis to the study of the behavioral correlates of afferent inhibition
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7694199/
https://www.ncbi.nlm.nih.gov/pubmed/33158104
http://dx.doi.org/10.3390/brainsci10110818
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