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. Crow...

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Autores principales:	Alban Bornet, Adrien Doerig, Michael H Herzog, Gregory Francis, Erik Van der Burg
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9033037d6a2746faaaee2c632edd2ffd
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id	oai:doaj.org-article:9033037d6a2746faaaee2c632edd2ffd
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spelling	oai:doaj.org-article:9033037d6a2746faaaee2c632edd2ffd2021-12-02T19:57:25ZShrinking Bouma's window: How to model crowding in dense displays.1553-734X1553-735810.1371/journal.pcbi.1009187https://doaj.org/article/9033037d6a2746faaaee2c632edd2ffd2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009187https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In 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.Alban BornetAdrien DoerigMichael H HerzogGregory FrancisErik Van der BurgPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009187 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Alban Bornet Adrien Doerig Michael H Herzog Gregory Francis Erik Van der Burg Shrinking Bouma's window: How to model crowding in dense displays.
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	article
author	Alban Bornet Adrien Doerig Michael H Herzog Gregory Francis Erik Van der Burg
author_facet	Alban Bornet Adrien Doerig Michael H Herzog Gregory Francis Erik Van der Burg
author_sort	Alban Bornet
title	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_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_sort	shrinking bouma's window: how to model crowding in dense displays.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9033037d6a2746faaaee2c632edd2ffd
work_keys_str_mv	AT albanbornet shrinkingboumaswindowhowtomodelcrowdingindensedisplays AT adriendoerig shrinkingboumaswindowhowtomodelcrowdingindensedisplays AT michaelhherzog shrinkingboumaswindowhowtomodelcrowdingindensedisplays AT gregoryfrancis shrinkingboumaswindowhowtomodelcrowdingindensedisplays AT erikvanderburg shrinkingboumaswindowhowtomodelcrowdingindensedisplays
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Shrinking Bouma's window: How to model crowding in dense displays.

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