Volume 7, Number 14, Article 11, Pages 1-13 doi:10.1167/7.14.11 http://journalofvision.org/7/14/11/ ISSN 1534-7362
Does spatial invariance result from insensitivity to change?
Frederick A. A. Kingdom
McGill Vision Research, Montréal, Québec, Canada
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David J. Field
Psychology Department, Cornell University, Ithaca, NY, USA
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Adriana Olmos
McGill Vision Research, Montréal, Québec, Canada
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Abstract

One of the fundamental unanswered questions in visual science regards how the visual system attains a high degree of invariance (e.g., position invariance, size invariance, etc.) while maintaining high selectivity. Although a variety of theories have been proposed, most are distinguished by the degree to which information is maintained or discarded. To test whether information is maintained or discarded, we have compared the ability of the human visual system to detect a variety of wide-field changes to natural images. The changes range from simple affine transforms and intensity changes common to our visual experience to random changes as represented by the addition of white noise. When sensitivity was measured in terms of the Euclidean distance (L2 norm) between image pairs, we found that observers were an order of magnitude less sensitive to the geometric transformations than to added noise. A control experiment ruled out that the sensitivity difference was caused by the statistical properties of the image difference created by this transformation. We argue that the remarkable difference in sensitivity relates to the processes used by the visual system to build invariant relationships and leads to the unusual result that observers are least sensitive to those transformations most commonly experienced in the natural world.

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History
Received August 7, 2006; published December 13, 2007
Citation
Kingdom, F. A. A., Field, D. J., & Olmos, A. (2007). Does spatial invariance result from insensitivity to change? Journal of Vision, 7(14):11, 1-13, http://journalofvision.org/7/14/11/, doi:10.1167/7.14.11.
Keywords
invariant transformations, natural scenes, geometric transformations, photometric transformations
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