August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
How is visual search guided by shape? Using features from deep learning to understand preattentive "shape space"
Author Affiliations
  • Krista Ehinger
    Harvard Medical School
  • Jeremy Wolfe
    Harvard Medical School
Journal of Vision September 2016, Vol.16, 695. doi:10.1167/16.12.695
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      Krista Ehinger, Jeremy Wolfe; How is visual search guided by shape? Using features from deep learning to understand preattentive "shape space". Journal of Vision 2016;16(12):695. doi: 10.1167/16.12.695.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Visual search can be guided by target shape, but our understanding of how shape guides search has been limited a set of specific shape features such as curvature, closure, line termination, aspect ratio, or intersection type (see Wolfe & Horowitz, 2004, for a review). These features, while important, do not capture the full range of preattentive shape processing. Understanding how shape guides search more generally requires a model of preattentive "shape space" that correctly represents the similarity between a target shape and different types of distractors. Here, we investigate whether the features learned by "deep learning" convolutional neural networks (CNNs) can be used as a proxy for this shape space. Previous work has shown that the visual representations learned by these networks generalize surprisingly well to a range of visual tasks (Razavian, Azizpour, Sullivan, & Carlsson, 2014). Eight participants performed a visual search task where they searched for a randomly-rotated shape target (a butterfly or rabbit silhouette) among different types of randomly-rotated distractor shapes generated from a family of radial frequency patterns. To characterize the distractor shapes, we ran them through a CNN (Krizhevsky, Sutskever, & Hinton, 2012) and used the feature vector produced by the second-to-last layer of the network as candidate shape features. Easy and hard distractors for each target were well-separated in this shape space, and hard distractors tended to be closer to the target in the neural network's representation of shape. Different participants tended to converge to a similar part of the feature space for hard distractors, but there was less agreement on which distractors were easiest. Our results suggest that the visual representation learned by a "deep learning" CNN is a reasonable approximation of the perceptual space in which humans process shape.

Meeting abstract presented at VSS 2016

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