A rodent model for the study of invariant visual object recognition
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Zoccolan-2009-A rodent model for t.pdf
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Author(s)
Zoccolan, Davide
DiCarlo, James
Oertelt, Nadja
Cox, David D.
Date Issued
April 2009
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
United States National Academy of Sciences
Citation
Zoccolan, Davide et al. “A rodent model for the study of invariant visual object recognition.” Proceedings of the National Academy of Sciences 106.21 (2009): 8748-8753. © 2009 National Academy of Sciences
Version
Final published version
Abstract
The human visual system is able to recognize objects despite tremendous variation in their appearance on the retina resulting from variation in view, size, lighting, etc. This ability—known as “invariant” object recognition—is central to visual perception, yet its computational underpinnings are poorly understood. Traditionally, nonhuman primates have been the animal model-of-choice for investigating the neuronal substrates of invariant recognition, because their visual systems closely mirror our own. Meanwhile, simpler and more accessible animal models such as rodents have been largely overlooked as possible models of higher-level visual functions, because their brains are often assumed to lack advanced visual processing machinery. As a result, little is known about rodents' ability to process complex visual stimuli in the face of real-world image variation. In the present work, we show that rats possess more advanced visual abilities than previously appreciated. Specifically, we trained pigmented rats to perform a visual task that required them to recognize objects despite substantial variation in their appearance, due to changes in size, view, and lighting. Critically, rats were able to spontaneously generalize to previously unseen transformations of learned objects. These results provide the first systematic evidence for invariant object recognition in rats and argue for an increased focus on rodents as models for studying high-level visual processing.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://dx.doi.org/10.1073/pnas.0811583106
