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Spatially intermixed objects of different categories are parsed automatically
Our visual system is able to separate spatially intermixed objects into different categorical groups (e.g., berries and leaves) using the shape of feature distribution: Determining whether all objects belong to one or several categories depends on whether the distribution has one or several peaks. D...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801410/ https://www.ncbi.nlm.nih.gov/pubmed/33432044 http://dx.doi.org/10.1038/s41598-020-79828-4 |
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author | Khvostov, Vladislav A. Lukashevich, Anton O. Utochkin, Igor S. |
author_facet | Khvostov, Vladislav A. Lukashevich, Anton O. Utochkin, Igor S. |
author_sort | Khvostov, Vladislav A. |
collection | PubMed |
description | Our visual system is able to separate spatially intermixed objects into different categorical groups (e.g., berries and leaves) using the shape of feature distribution: Determining whether all objects belong to one or several categories depends on whether the distribution has one or several peaks. Despite the apparent ease of rapid categorization, it is a very computationally demanding task, given severely limited “bottlenecks” of attention and working memory capable of processing only a few objects at a time. Here, we tested whether this rapid categorical parsing is automatic or requires attention. We used the visual mismatch negativity (vMMN) ERP component known as a marker of automatic sensory discrimination. 20 volunteers (16 female, mean age—22.7) participated in our study. Loading participants’ attention with a central task, we observed a substantial vMMN response to unattended background changes of categories defined by certain length-orientation conjunctions. Importantly, this occurred in conditions where the distributions of these features had several peaks and, hence, supported categorical separation. These results suggest that spatially intermixed objects are parsed into distinct categories automatically and give new insight into how the visual system can bypass the severe processing restrictions and form rich perceptual experience. |
format | Online Article Text |
id | pubmed-7801410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78014102021-01-12 Spatially intermixed objects of different categories are parsed automatically Khvostov, Vladislav A. Lukashevich, Anton O. Utochkin, Igor S. Sci Rep Article Our visual system is able to separate spatially intermixed objects into different categorical groups (e.g., berries and leaves) using the shape of feature distribution: Determining whether all objects belong to one or several categories depends on whether the distribution has one or several peaks. Despite the apparent ease of rapid categorization, it is a very computationally demanding task, given severely limited “bottlenecks” of attention and working memory capable of processing only a few objects at a time. Here, we tested whether this rapid categorical parsing is automatic or requires attention. We used the visual mismatch negativity (vMMN) ERP component known as a marker of automatic sensory discrimination. 20 volunteers (16 female, mean age—22.7) participated in our study. Loading participants’ attention with a central task, we observed a substantial vMMN response to unattended background changes of categories defined by certain length-orientation conjunctions. Importantly, this occurred in conditions where the distributions of these features had several peaks and, hence, supported categorical separation. These results suggest that spatially intermixed objects are parsed into distinct categories automatically and give new insight into how the visual system can bypass the severe processing restrictions and form rich perceptual experience. Nature Publishing Group UK 2021-01-11 /pmc/articles/PMC7801410/ /pubmed/33432044 http://dx.doi.org/10.1038/s41598-020-79828-4 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Khvostov, Vladislav A. Lukashevich, Anton O. Utochkin, Igor S. Spatially intermixed objects of different categories are parsed automatically |
title | Spatially intermixed objects of different categories are parsed automatically |
title_full | Spatially intermixed objects of different categories are parsed automatically |
title_fullStr | Spatially intermixed objects of different categories are parsed automatically |
title_full_unstemmed | Spatially intermixed objects of different categories are parsed automatically |
title_short | Spatially intermixed objects of different categories are parsed automatically |
title_sort | spatially intermixed objects of different categories are parsed automatically |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801410/ https://www.ncbi.nlm.nih.gov/pubmed/33432044 http://dx.doi.org/10.1038/s41598-020-79828-4 |
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