↓ Skip to main content

PLOS

Brain Cells in the Avian ‘Prefrontal Cortex’ Code for Features of Slot-Machine-Like Gambling

Overview of attention for article published in PLOS ONE, January 2011
Altmetric Badge

Mentioned by

reddit
1 Redditor

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
39 Mendeley
citeulike
1 CiteULike
Title
Brain Cells in the Avian ‘Prefrontal Cortex’ Code for Features of Slot-Machine-Like Gambling
Published in
PLOS ONE, January 2011
DOI 10.1371/journal.pone.0014589
Pubmed ID
Authors

Damian Scarf, Kirby Miles, Amanda Sloan, Natalie Goulter, Matt Hegan, Azade Seid-Fatemi, David Harper, Michael Colombo

Abstract

Slot machines are the most common and addictive form of gambling. In the current study, we recorded from single neurons in the 'prefrontal cortex' of pigeons while they played a slot-machine-like task. We identified four categories of neurons that coded for different aspects of our slot-machine-like task. Reward-Proximity neurons showed a linear increase in activity as the opportunity for a reward drew near. I-Won neurons fired only when the fourth stimulus of a winning (four-of-a-kind) combination was displayed. I-Lost neurons changed their firing rate at the presentation of the first nonidentical stimulus, that is, when it was apparent that no reward was forthcoming. Finally, Near-Miss neurons also changed their activity the moment it was recognized that a reward was no longer available, but more importantly, the activity level was related to whether the trial contained one, two, or three identical stimuli prior to the display of the nonidentical stimulus. These findings not only add to recent neurophysiological research employing simulated gambling paradigms, but also add to research addressing the functional correspondence between the avian NCL and primate PFC.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 3%
Unknown 38 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 26%
Student > Ph. D. Student 6 15%
Student > Master 6 15%
Lecturer > Senior Lecturer 3 8%
Student > Postgraduate 3 8%
Other 8 21%
Unknown 3 8%
Readers by discipline Count As %
Psychology 13 33%
Neuroscience 10 26%
Medicine and Dentistry 4 10%
Agricultural and Biological Sciences 3 8%
Social Sciences 2 5%
Other 2 5%
Unknown 5 13%