The Müller-Lyer Illusion in a computational model of biological object recognition.

Studying illusions provides insight into the way the brain processes information. The Müller-Lyer Illusion (MLI) is a classical geometrical illusion of size, in which perceived line length is decreased by arrowheads and increased by arrowtails. Many theories have been put forward to explain the MLI,...

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Autores principales:	Astrid Zeman, Oliver Obst, Kevin R Brooks, Anina N Rich
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/111183ff5c4a4cfeaf891a63c420f5f9
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spelling	oai:doaj.org-article:111183ff5c4a4cfeaf891a63c420f5f92021-11-18T07:57:22ZThe Müller-Lyer Illusion in a computational model of biological object recognition.1932-620310.1371/journal.pone.0056126https://doaj.org/article/111183ff5c4a4cfeaf891a63c420f5f92013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23457510/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Studying illusions provides insight into the way the brain processes information. The Müller-Lyer Illusion (MLI) is a classical geometrical illusion of size, in which perceived line length is decreased by arrowheads and increased by arrowtails. Many theories have been put forward to explain the MLI, such as misapplied size constancy scaling, the statistics of image-source relationships and the filtering properties of signal processing in primary visual areas. Artificial models of the ventral visual processing stream allow us to isolate factors hypothesised to cause the illusion and test how these affect classification performance. We trained a feed-forward feature hierarchical model, HMAX, to perform a dual category line length judgment task (short versus long) with over 90% accuracy. We then tested the system in its ability to judge relative line lengths for images in a control set versus images that induce the MLI in humans. Results from the computational model show an overall illusory effect similar to that experienced by human subjects. No natural images were used for training, implying that misapplied size constancy and image-source statistics are not necessary factors for generating the illusion. A post-hoc analysis of response weights within a representative trained network ruled out the possibility that the illusion is caused by a reliance on information at low spatial frequencies. Our results suggest that the MLI can be produced using only feed-forward, neurophysiological connections.Astrid ZemanOliver ObstKevin R BrooksAnina N RichPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e56126 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Astrid Zeman Oliver Obst Kevin R Brooks Anina N Rich The Müller-Lyer Illusion in a computational model of biological object recognition.
description	Studying illusions provides insight into the way the brain processes information. The Müller-Lyer Illusion (MLI) is a classical geometrical illusion of size, in which perceived line length is decreased by arrowheads and increased by arrowtails. Many theories have been put forward to explain the MLI, such as misapplied size constancy scaling, the statistics of image-source relationships and the filtering properties of signal processing in primary visual areas. Artificial models of the ventral visual processing stream allow us to isolate factors hypothesised to cause the illusion and test how these affect classification performance. We trained a feed-forward feature hierarchical model, HMAX, to perform a dual category line length judgment task (short versus long) with over 90% accuracy. We then tested the system in its ability to judge relative line lengths for images in a control set versus images that induce the MLI in humans. Results from the computational model show an overall illusory effect similar to that experienced by human subjects. No natural images were used for training, implying that misapplied size constancy and image-source statistics are not necessary factors for generating the illusion. A post-hoc analysis of response weights within a representative trained network ruled out the possibility that the illusion is caused by a reliance on information at low spatial frequencies. Our results suggest that the MLI can be produced using only feed-forward, neurophysiological connections.
format	article
author	Astrid Zeman Oliver Obst Kevin R Brooks Anina N Rich
author_facet	Astrid Zeman Oliver Obst Kevin R Brooks Anina N Rich
author_sort	Astrid Zeman
title	The Müller-Lyer Illusion in a computational model of biological object recognition.
title_short	The Müller-Lyer Illusion in a computational model of biological object recognition.
title_full	The Müller-Lyer Illusion in a computational model of biological object recognition.
title_fullStr	The Müller-Lyer Illusion in a computational model of biological object recognition.
title_full_unstemmed	The Müller-Lyer Illusion in a computational model of biological object recognition.
title_sort	müller-lyer illusion in a computational model of biological object recognition.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/111183ff5c4a4cfeaf891a63c420f5f9
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The Müller-Lyer Illusion in a computational model of biological object recognition.

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