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Crowding and attention in a framework of neural network model
In this article, I present a framework that would accommodate the classic ideas of visual information processing together with more recent computational approaches. I used the current knowledge about visual crowding, capacity limitations, attention, and saliency to place these phenomena within a sta...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The Association for Research in Vision and Ophthalmology
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774107/ https://www.ncbi.nlm.nih.gov/pubmed/33372986 http://dx.doi.org/10.1167/jov.20.13.19 |
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author | Põder, Endel |
author_facet | Põder, Endel |
author_sort | Põder, Endel |
collection | PubMed |
description | In this article, I present a framework that would accommodate the classic ideas of visual information processing together with more recent computational approaches. I used the current knowledge about visual crowding, capacity limitations, attention, and saliency to place these phenomena within a standard neural network model. I suggest some revisions to traditional mechanisms of attention and feature integration that are required to fit better into this framework. The results allow us to explain some apparent theoretical controversies in vision research, suggesting a rationale for the limited spatial extent of crowding, a role of saliency in crowding experiments, and several amendments to the feature integration theory. The scheme can be elaborated or modified by future research. |
format | Online Article Text |
id | pubmed-7774107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-77741072021-01-13 Crowding and attention in a framework of neural network model Põder, Endel J Vis Perspective In this article, I present a framework that would accommodate the classic ideas of visual information processing together with more recent computational approaches. I used the current knowledge about visual crowding, capacity limitations, attention, and saliency to place these phenomena within a standard neural network model. I suggest some revisions to traditional mechanisms of attention and feature integration that are required to fit better into this framework. The results allow us to explain some apparent theoretical controversies in vision research, suggesting a rationale for the limited spatial extent of crowding, a role of saliency in crowding experiments, and several amendments to the feature integration theory. The scheme can be elaborated or modified by future research. The Association for Research in Vision and Ophthalmology 2020-12-29 /pmc/articles/PMC7774107/ /pubmed/33372986 http://dx.doi.org/10.1167/jov.20.13.19 Text en Copyright 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Perspective Põder, Endel Crowding and attention in a framework of neural network model |
title | Crowding and attention in a framework of neural network model |
title_full | Crowding and attention in a framework of neural network model |
title_fullStr | Crowding and attention in a framework of neural network model |
title_full_unstemmed | Crowding and attention in a framework of neural network model |
title_short | Crowding and attention in a framework of neural network model |
title_sort | crowding and attention in a framework of neural network model |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774107/ https://www.ncbi.nlm.nih.gov/pubmed/33372986 http://dx.doi.org/10.1167/jov.20.13.19 |
work_keys_str_mv | AT poderendel crowdingandattentioninaframeworkofneuralnetworkmodel |