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Crowding results from optimal integration of visual targets with contextual information
Crowding is the inability to recognize an object in clutter, usually considered a fundamental low-level bottleneck to object recognition. Here we advance and test an alternative idea, that crowding, like predictive phenomena such as serial dependence, results from optimizing strategies that exploit...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525686/ https://www.ncbi.nlm.nih.gov/pubmed/36180497 http://dx.doi.org/10.1038/s41467-022-33508-1 |
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author | Cicchini, Guido Marco D’Errico, Giovanni Burr, David Charles |
author_facet | Cicchini, Guido Marco D’Errico, Giovanni Burr, David Charles |
author_sort | Cicchini, Guido Marco |
collection | PubMed |
description | Crowding is the inability to recognize an object in clutter, usually considered a fundamental low-level bottleneck to object recognition. Here we advance and test an alternative idea, that crowding, like predictive phenomena such as serial dependence, results from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions: crowding should be greatest for unreliable targets and reliable flankers; crowding-induced biases should be maximal when target and flankers have similar orientations, falling off for differences around 20°; flanker interference should be associated with higher precision in orientation judgements, leading to lower overall error rate; effects should be maximal when the orientation of the target is near that of the average of the flankers, rather than to that of individual flankers. Each of these predictions were supported, and could be simulated with ideal-observer models that maximize performance. The results suggest that while crowding can affect object recognition, it may be better understood not as a processing bottleneck, but as a consequence of efficient exploitation of the spatial redundancies of the natural world. |
format | Online Article Text |
id | pubmed-9525686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95256862022-10-02 Crowding results from optimal integration of visual targets with contextual information Cicchini, Guido Marco D’Errico, Giovanni Burr, David Charles Nat Commun Article Crowding is the inability to recognize an object in clutter, usually considered a fundamental low-level bottleneck to object recognition. Here we advance and test an alternative idea, that crowding, like predictive phenomena such as serial dependence, results from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions: crowding should be greatest for unreliable targets and reliable flankers; crowding-induced biases should be maximal when target and flankers have similar orientations, falling off for differences around 20°; flanker interference should be associated with higher precision in orientation judgements, leading to lower overall error rate; effects should be maximal when the orientation of the target is near that of the average of the flankers, rather than to that of individual flankers. Each of these predictions were supported, and could be simulated with ideal-observer models that maximize performance. The results suggest that while crowding can affect object recognition, it may be better understood not as a processing bottleneck, but as a consequence of efficient exploitation of the spatial redundancies of the natural world. Nature Publishing Group UK 2022-09-30 /pmc/articles/PMC9525686/ /pubmed/36180497 http://dx.doi.org/10.1038/s41467-022-33508-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cicchini, Guido Marco D’Errico, Giovanni Burr, David Charles Crowding results from optimal integration of visual targets with contextual information |
title | Crowding results from optimal integration of visual targets with contextual information |
title_full | Crowding results from optimal integration of visual targets with contextual information |
title_fullStr | Crowding results from optimal integration of visual targets with contextual information |
title_full_unstemmed | Crowding results from optimal integration of visual targets with contextual information |
title_short | Crowding results from optimal integration of visual targets with contextual information |
title_sort | crowding results from optimal integration of visual targets with contextual information |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525686/ https://www.ncbi.nlm.nih.gov/pubmed/36180497 http://dx.doi.org/10.1038/s41467-022-33508-1 |
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