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Shrinking Bouma’s window: How to model crowding in dense displays
In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma’s law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284675/ https://www.ncbi.nlm.nih.gov/pubmed/34228703 http://dx.doi.org/10.1371/journal.pcbi.1009187 |
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author | Bornet, Alban Doerig, Adrien Herzog, Michael H. Francis, Gregory Van der Burg, Erik |
author_facet | Bornet, Alban Doerig, Adrien Herzog, Michael H. Francis, Gregory Van der Burg, Erik |
author_sort | Bornet, Alban |
collection | PubMed |
description | In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma’s law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma’s law, only the target’s nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model’s outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage. |
format | Online Article Text |
id | pubmed-8284675 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82846752021-07-28 Shrinking Bouma’s window: How to model crowding in dense displays Bornet, Alban Doerig, Adrien Herzog, Michael H. Francis, Gregory Van der Burg, Erik PLoS Comput Biol Research Article In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma’s law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma’s law, only the target’s nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model’s outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage. Public Library of Science 2021-07-06 /pmc/articles/PMC8284675/ /pubmed/34228703 http://dx.doi.org/10.1371/journal.pcbi.1009187 Text en © 2021 Bornet et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bornet, Alban Doerig, Adrien Herzog, Michael H. Francis, Gregory Van der Burg, Erik Shrinking Bouma’s window: How to model crowding in dense displays |
title | Shrinking Bouma’s window: How to model crowding in dense displays |
title_full | Shrinking Bouma’s window: How to model crowding in dense displays |
title_fullStr | Shrinking Bouma’s window: How to model crowding in dense displays |
title_full_unstemmed | Shrinking Bouma’s window: How to model crowding in dense displays |
title_short | Shrinking Bouma’s window: How to model crowding in dense displays |
title_sort | shrinking bouma’s window: how to model crowding in dense displays |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284675/ https://www.ncbi.nlm.nih.gov/pubmed/34228703 http://dx.doi.org/10.1371/journal.pcbi.1009187 |
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