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Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment

Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and de...

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Autores principales: Daikoku, Tatsuya, Kamermans, Kevin, Minatoya, Maiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Leibniz Research Centre for Working Environment and Human Factors 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502202/
https://www.ncbi.nlm.nih.gov/pubmed/37720236
http://dx.doi.org/10.17179/excli2023-6135
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author Daikoku, Tatsuya
Kamermans, Kevin
Minatoya, Maiko
author_facet Daikoku, Tatsuya
Kamermans, Kevin
Minatoya, Maiko
author_sort Daikoku, Tatsuya
collection PubMed
description Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "reliability" of prediction, 2) the construction of information “hierarchy” through chunking, and 3) the acquisition of “1-3Hz rhythm” that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts.
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spelling pubmed-105022022023-09-16 Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment Daikoku, Tatsuya Kamermans, Kevin Minatoya, Maiko EXCLI J Review Article Statistical learning starts at an early age and is intimately linked to brain development and the emergence of individuality. Through such a long period of statistical learning, the brain updates and constructs statistical models, with the model's individuality changing based on the type and degree of stimulation received. However, the detailed mechanisms underlying this process are unknown. This paper argues three main points of statistical learning, including 1) cognitive individuality based on "reliability" of prediction, 2) the construction of information “hierarchy” through chunking, and 3) the acquisition of “1-3Hz rhythm” that is essential for early language and music learning. We developed a Hierarchical Bayesian Statistical Learning (HBSL) model that takes into account both reliability and hierarchy, mimicking the statistical learning processes of the brain. Using this model, we conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning. By modulating the sensitivity to sound stimuli, we simulated three cognitive models with different reliability on bottom-up sensory stimuli relative to top-down prior prediction: hypo-sensitive, normal-sensitive, and hyper-sensitive models. We suggested that statistical learning plays a crucial role in the acquisition of 1-3 Hz rhythm. Moreover, a hyper-sensitive model quickly learned the sensory statistics but became fixated on their internal model, making it difficult to generate new information, whereas a hypo-sensitive model has lower learning efficiency but may be more likely to generate new information. Various individual characteristics may not necessarily confer an overall advantage over others, as there may be a trade-off between learning efficiency and the ease of generating new information. This study has the potential to shed light on the heterogeneous nature of statistical learning, as well as the paradoxical phenomenon in which individuals with certain cognitive traits that impede specific types of perceptual abilities exhibit superior performance in creative contexts. Leibniz Research Centre for Working Environment and Human Factors 2023-08-04 /pmc/articles/PMC10502202/ /pubmed/37720236 http://dx.doi.org/10.17179/excli2023-6135 Text en Copyright © 2023 Daikoku et al. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ) You are free to copy, distribute and transmit the work, provided the original author and source are credited.
spellingShingle Review Article
Daikoku, Tatsuya
Kamermans, Kevin
Minatoya, Maiko
Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment
title Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment
title_full Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment
title_fullStr Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment
title_full_unstemmed Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment
title_short Exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment
title_sort exploring cognitive individuality and the underlying creativity in statistical learning and phase entrainment
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502202/
https://www.ncbi.nlm.nih.gov/pubmed/37720236
http://dx.doi.org/10.17179/excli2023-6135
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