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A Model for Statistical Regularity Extraction from Dynamic Sounds

To understand our surroundings, we effortlessly parse our sound environment into sound sources, extracting invariant information—or regularities—over time to build an internal representation of the world around us. Previous experimental work has shown the brain is sensitive to many types of regulari...

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Detalles Bibliográficos
Autores principales: Skerritt-Davis, Benjamin, Elhilali, Mounya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953992/
https://www.ncbi.nlm.nih.gov/pubmed/31929768
http://dx.doi.org/10.3813/AAA.919279
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author Skerritt-Davis, Benjamin
Elhilali, Mounya
author_facet Skerritt-Davis, Benjamin
Elhilali, Mounya
author_sort Skerritt-Davis, Benjamin
collection PubMed
description To understand our surroundings, we effortlessly parse our sound environment into sound sources, extracting invariant information—or regularities—over time to build an internal representation of the world around us. Previous experimental work has shown the brain is sensitive to many types of regularities in sound, but theoretical models that capture underlying principles of regularity tracking across diverse sequence structures have been few and far between. Existing efforts often focus on sound patterns rather the stochastic nature of sequences. In the current study, we employ a perceptual model for regularity extraction based on a Bayesian framework that posits the brain collects statistical information over time. We show this model can be used to simulate various results from the literature with stimuli exhibiting a wide range of predictability. This model can provide a useful tool for both interpreting existing experimental results under a unified model and providing predictions for new ones using more complex stimuli.
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spelling pubmed-69539922020-01-10 A Model for Statistical Regularity Extraction from Dynamic Sounds Skerritt-Davis, Benjamin Elhilali, Mounya Acta Acust United Acust Article To understand our surroundings, we effortlessly parse our sound environment into sound sources, extracting invariant information—or regularities—over time to build an internal representation of the world around us. Previous experimental work has shown the brain is sensitive to many types of regularities in sound, but theoretical models that capture underlying principles of regularity tracking across diverse sequence structures have been few and far between. Existing efforts often focus on sound patterns rather the stochastic nature of sequences. In the current study, we employ a perceptual model for regularity extraction based on a Bayesian framework that posits the brain collects statistical information over time. We show this model can be used to simulate various results from the literature with stimuli exhibiting a wide range of predictability. This model can provide a useful tool for both interpreting existing experimental results under a unified model and providing predictions for new ones using more complex stimuli. 2018-12-07 2019 /pmc/articles/PMC6953992/ /pubmed/31929768 http://dx.doi.org/10.3813/AAA.919279 Text en https://creativecommons.org/licenses/by/4.0/ This is an open access article under the terms of the Creative Commons Attribution (CC BY 4.0) license.
spellingShingle Article
Skerritt-Davis, Benjamin
Elhilali, Mounya
A Model for Statistical Regularity Extraction from Dynamic Sounds
title A Model for Statistical Regularity Extraction from Dynamic Sounds
title_full A Model for Statistical Regularity Extraction from Dynamic Sounds
title_fullStr A Model for Statistical Regularity Extraction from Dynamic Sounds
title_full_unstemmed A Model for Statistical Regularity Extraction from Dynamic Sounds
title_short A Model for Statistical Regularity Extraction from Dynamic Sounds
title_sort model for statistical regularity extraction from dynamic sounds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953992/
https://www.ncbi.nlm.nih.gov/pubmed/31929768
http://dx.doi.org/10.3813/AAA.919279
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