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Gambling in the Visual Periphery: A Conjoint-Measurement Analysis of Human Ability to Judge Visual Uncertainty

Overview of attention for article published in PLoS Computational Biology, December 2010
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Title
Gambling in the Visual Periphery: A Conjoint-Measurement Analysis of Human Ability to Judge Visual Uncertainty
Published in
PLoS Computational Biology, December 2010
DOI 10.1371/journal.pcbi.1001023
Pubmed ID
Authors

Hang Zhang, Camille Morvan, Laurence T. Maloney

Abstract

Recent work in motor control demonstrates that humans take their own motor uncertainty into account, adjusting the timing and goals of movement so as to maximize expected gain. Visual sensitivity varies dramatically with retinal location and target, and models of optimal visual search typically assume that the visual system takes retinal inhomogeneity into account in planning eye movements. Such models can then use the entire retina rather than just the fovea to speed search. Using a simple decision task, we evaluated human ability to compensate for retinal inhomogeneity. We first measured observers' sensitivity for targets, varying contrast and eccentricity. Observers then repeatedly chose between targets differing in eccentricity and contrast, selecting the one they would prefer to attempt: e.g., a low contrast target at 2° versus a high contrast target at 10°. Observers knew they would later attempt some of their chosen targets and receive rewards for correct classifications. We evaluated performance in three ways. Equivalence: Do observers' judgments agree with their actual performance? Do they correctly trade off eccentricity and contrast and select the more discriminable target in each pair? Transitivity: Are observers' choices self-consistent? Dominance: Do observers understand that increased contrast improves performance? Decreased eccentricity? All observers exhibited patterned failures of equivalence, and seven out of eight observers failed transitivity. There were significant but small failures of dominance. All these failures together reduced their winnings by 10%-18%.

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Geographical breakdown

Country Count As %
United States 4 6%
Hungary 1 2%
Italy 1 2%
Switzerland 1 2%
Argentina 1 2%
United Kingdom 1 2%
Unknown 57 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 23%
Student > Ph. D. Student 14 21%
Student > Master 8 12%
Student > Bachelor 5 8%
Student > Doctoral Student 3 5%
Other 12 18%
Unknown 9 14%
Readers by discipline Count As %
Psychology 23 35%
Agricultural and Biological Sciences 10 15%
Computer Science 7 11%
Neuroscience 4 6%
Nursing and Health Professions 3 5%
Other 11 17%
Unknown 8 12%