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An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners

Relational models of cognition provide parsimonious and actionable models of generative behavior witnessed in humans. They also inform many current computational analogs of cognition including Deep Neural Networks, Reinforcement Learning algorithms, Self-Organizing Maps, as well as blended architect...

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Detalles Bibliográficos
Autores principales: Smith, Patrick, Hayes, Steven C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240703/
https://www.ncbi.nlm.nih.gov/pubmed/35783756
http://dx.doi.org/10.3389/fpsyg.2022.914485
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author Smith, Patrick
Hayes, Steven C.
author_facet Smith, Patrick
Hayes, Steven C.
author_sort Smith, Patrick
collection PubMed
description Relational models of cognition provide parsimonious and actionable models of generative behavior witnessed in humans. They also inform many current computational analogs of cognition including Deep Neural Networks, Reinforcement Learning algorithms, Self-Organizing Maps, as well as blended architectures that are outperforming traditional semantic models. The black box nature of these computer models artificially limits scientific and applied progress and human computer interaction. This paper presents a first in the field attempt to model relational processes using logical derivation scripts and network graph visualizations written in the open-source R language. These tools are presented as a way for researchers and practitioners to begin to explore more complex relational models in a manner that can advance the theory and empirical science, as well as prepare the field for future collaborations with advanced computational models of cognition.
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spelling pubmed-92407032022-06-30 An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners Smith, Patrick Hayes, Steven C. Front Psychol Psychology Relational models of cognition provide parsimonious and actionable models of generative behavior witnessed in humans. They also inform many current computational analogs of cognition including Deep Neural Networks, Reinforcement Learning algorithms, Self-Organizing Maps, as well as blended architectures that are outperforming traditional semantic models. The black box nature of these computer models artificially limits scientific and applied progress and human computer interaction. This paper presents a first in the field attempt to model relational processes using logical derivation scripts and network graph visualizations written in the open-source R language. These tools are presented as a way for researchers and practitioners to begin to explore more complex relational models in a manner that can advance the theory and empirical science, as well as prepare the field for future collaborations with advanced computational models of cognition. Frontiers Media S.A. 2022-06-15 /pmc/articles/PMC9240703/ /pubmed/35783756 http://dx.doi.org/10.3389/fpsyg.2022.914485 Text en Copyright © 2022 Smith and Hayes. https://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
Smith, Patrick
Hayes, Steven C.
An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners
title An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners
title_full An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners
title_fullStr An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners
title_full_unstemmed An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners
title_short An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners
title_sort open-source relational network derivation script in r for modeling and visualizing complex behavior for scientists and practitioners
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240703/
https://www.ncbi.nlm.nih.gov/pubmed/35783756
http://dx.doi.org/10.3389/fpsyg.2022.914485
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