Volume 2, Number 1, Article 3, Pages 25-45 doi:10.1167/2.1.3 http://journalofvision.org/2/1/3/ ISSN 1534-7362
The footprints of visual attention in the Posner cueing paradigm revealed by classification images
Miguel P. Eckstein
Department of Psychology, University of California, Santa Barbara, CA, USA
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Steven S. Shimozaki
Department of Psychology, University of California, Santa Barbara, CA, USA
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Craig K. Abbey
Dept. of Biomedical Engineering, University of California, Davis, CA, USA
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Abstract

In the Posner cueing paradigm, observers’ performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

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History
Received June 30, 2001; published January 16, 2002
Citation
Eckstein, M. P., Shimozaki, S. S., & Abbey, C. K. (2002). The footprints of visual attention in the Posner cueing paradigm revealed by classification images. Journal of Vision, 2(1):3, 25-45, http://journalofvision.org/2/1/3/, doi:10.1167/2.1.3.
Keywords
attention, computational modeling, ideal observer, noise, cueing paradigm
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