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Spatial Probability Dynamically Modulates Visual Target Detection in Chickens

Overview of attention for article published in PLOS ONE, May 2013
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Title
Spatial Probability Dynamically Modulates Visual Target Detection in Chickens
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0064136
Pubmed ID
Authors

Devarajan Sridharan, Deepa L. Ramamurthy, Eric I. Knudsen

Abstract

The natural world contains a rich and ever-changing landscape of sensory information. To survive, an organism must be able to flexibly and rapidly locate the most relevant sources of information at any time. Humans and non-human primates exploit regularities in the spatial distribution of relevant stimuli (targets) to improve detection at locations of high target probability. Is the ability to flexibly modify behavior based on visual experience unique to primates? Chickens (Gallus domesticus) were trained on a multiple alternative Go/NoGo task to detect a small, briefly-flashed dot (target) in each of the quadrants of the visual field. When targets were presented with equal probability (25%) in each quadrant, chickens exhibited a distinct advantage for detecting targets at lower, relative to upper, hemifield locations. Increasing the probability of presentation in the upper hemifield locations (to 80%) dramatically improved detection performance at these locations to be on par with lower hemifield performance. Finally, detection performance in the upper hemifield changed on a rapid timescale, improving with successive target detections, and declining with successive detections at the diagonally opposite location in the lower hemifield. These data indicate the action of a process that in chickens, as in primates, flexibly and dynamically modulates detection performance based on the spatial probabilities of sensory stimuli as well as on recent performance history.

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Geographical breakdown

Country Count As %
United States 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 32%
Student > Ph. D. Student 9 29%
Student > Doctoral Student 3 10%
Professor > Associate Professor 3 10%
Student > Bachelor 1 3%
Other 1 3%
Unknown 4 13%
Readers by discipline Count As %
Neuroscience 9 29%
Agricultural and Biological Sciences 5 16%
Psychology 5 16%
Medicine and Dentistry 2 6%
Computer Science 1 3%
Other 3 10%
Unknown 6 19%