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Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure

Successive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poo...

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Autores principales: Benjamin, Lucas, Fló, Ana, Al Roumi, Fosca, Dehaene-Lambertz, Ghislaine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241517/
https://www.ncbi.nlm.nih.gov/pubmed/37129367
http://dx.doi.org/10.7554/eLife.86430
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author Benjamin, Lucas
Fló, Ana
Al Roumi, Fosca
Dehaene-Lambertz, Ghislaine
author_facet Benjamin, Lucas
Fló, Ana
Al Roumi, Fosca
Dehaene-Lambertz, Ghislaine
author_sort Benjamin, Lucas
collection PubMed
description Successive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poorly understood. Here, we used the formalism proposed by network science to study the representation of local and higher-order structures and their interaction in auditory sequences. We show that human adults exhibited biases in their perception of local transitions between elements, which made them sensitive to high-order network structures such as communities. This behavior is consistent with the creation of a parsimonious simplified model from the evidence they receive, achieved by pruning and completing relationships between network elements. This observation suggests that the brain does not rely on exact memories but on a parsimonious representation of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly accounts for previous findings, including local transition probabilities as well as high-order network structures, unifying sequence learning across scales. We finally propose putative brain implementations of such bias.
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spelling pubmed-102415172023-06-06 Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure Benjamin, Lucas Fló, Ana Al Roumi, Fosca Dehaene-Lambertz, Ghislaine eLife Neuroscience Successive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poorly understood. Here, we used the formalism proposed by network science to study the representation of local and higher-order structures and their interaction in auditory sequences. We show that human adults exhibited biases in their perception of local transitions between elements, which made them sensitive to high-order network structures such as communities. This behavior is consistent with the creation of a parsimonious simplified model from the evidence they receive, achieved by pruning and completing relationships between network elements. This observation suggests that the brain does not rely on exact memories but on a parsimonious representation of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly accounts for previous findings, including local transition probabilities as well as high-order network structures, unifying sequence learning across scales. We finally propose putative brain implementations of such bias. eLife Sciences Publications, Ltd 2023-05-02 /pmc/articles/PMC10241517/ /pubmed/37129367 http://dx.doi.org/10.7554/eLife.86430 Text en © 2023, Benjamin et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Benjamin, Lucas
Fló, Ana
Al Roumi, Fosca
Dehaene-Lambertz, Ghislaine
Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
title Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
title_full Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
title_fullStr Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
title_full_unstemmed Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
title_short Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
title_sort humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241517/
https://www.ncbi.nlm.nih.gov/pubmed/37129367
http://dx.doi.org/10.7554/eLife.86430
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