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Analog Resonance Computation: A New Model for Human Cognition

Early models of human cognition appeared to posit the brain as a collection of discrete digital computing modules with specific data processing functions. More recent theories such as the Hierarchically Mechanistic Mind characterize the brain as a massive hierarchy of interconnected and adaptive cir...

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Autor principal: Byrne, Aidan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509107/
https://www.ncbi.nlm.nih.gov/pubmed/33013530
http://dx.doi.org/10.3389/fpsyg.2020.02080
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author Byrne, Aidan J.
author_facet Byrne, Aidan J.
author_sort Byrne, Aidan J.
collection PubMed
description Early models of human cognition appeared to posit the brain as a collection of discrete digital computing modules with specific data processing functions. More recent theories such as the Hierarchically Mechanistic Mind characterize the brain as a massive hierarchy of interconnected and adaptive circuits whose primary aim is to reduce entropy. However, studies in high workload/stress situations show that human behavior is often error prone and seemingly irrational. Rather than regarding such behavior to be uncharacteristic, this paper suggest that such “atypical” behavior provides the best information on which to base theories of human cognition. Rather than using a digital paradigm, human cognition should be seen as an analog computer based on resonating circuits whose primary driver is to constantly extract information from the massively complex and rapidly changing world around us to construct an internal model of reality that allows us to rapidly respond to the threats and opportunities.
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spelling pubmed-75091072020-10-02 Analog Resonance Computation: A New Model for Human Cognition Byrne, Aidan J. Front Psychol Psychology Early models of human cognition appeared to posit the brain as a collection of discrete digital computing modules with specific data processing functions. More recent theories such as the Hierarchically Mechanistic Mind characterize the brain as a massive hierarchy of interconnected and adaptive circuits whose primary aim is to reduce entropy. However, studies in high workload/stress situations show that human behavior is often error prone and seemingly irrational. Rather than regarding such behavior to be uncharacteristic, this paper suggest that such “atypical” behavior provides the best information on which to base theories of human cognition. Rather than using a digital paradigm, human cognition should be seen as an analog computer based on resonating circuits whose primary driver is to constantly extract information from the massively complex and rapidly changing world around us to construct an internal model of reality that allows us to rapidly respond to the threats and opportunities. Frontiers Media S.A. 2020-09-09 /pmc/articles/PMC7509107/ /pubmed/33013530 http://dx.doi.org/10.3389/fpsyg.2020.02080 Text en Copyright © 2020 Byrne. http://creativecommons.org/licenses/by/4.0/ 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) and the copyright owner(s) 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.
spellingShingle Psychology
Byrne, Aidan J.
Analog Resonance Computation: A New Model for Human Cognition
title Analog Resonance Computation: A New Model for Human Cognition
title_full Analog Resonance Computation: A New Model for Human Cognition
title_fullStr Analog Resonance Computation: A New Model for Human Cognition
title_full_unstemmed Analog Resonance Computation: A New Model for Human Cognition
title_short Analog Resonance Computation: A New Model for Human Cognition
title_sort analog resonance computation: a new model for human cognition
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509107/
https://www.ncbi.nlm.nih.gov/pubmed/33013530
http://dx.doi.org/10.3389/fpsyg.2020.02080
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