The Müller-lyer illusion in a computational model of biological object recognition

Astrid Zeman, Oliver Obst, Kevin R. Brooks, Anina N. Rich

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

LanguageEnglish
Article numbere56126
Pages1-9
Number of pages9
JournalPLoS ONE
Volume8
Issue number2
DOIs
Publication statusPublished - 15 Feb 2013

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Biological Models
Object recognition
statistics
Statistics
Brain
Signal processing
brain
Sagittaria
Processing
Recognition (Psychology)
biological models
Aptitude
testing
Weights and Measures

Bibliographical note

Copyright the Author(s) 2013. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Cite this

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The Müller-lyer illusion in a computational model of biological object recognition. / Zeman, Astrid; Obst, Oliver; Brooks, Kevin R.; Rich, Anina N.

In: PLoS ONE, Vol. 8, No. 2, e56126, 15.02.2013, p. 1-9.

Research output: Contribution to journalArticleResearchpeer-review

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