Journal
PSYCHOLOGICAL REVIEW
Volume 114, Issue 2, Pages 376-397Publisher
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/0033-295X.114.2.376
Keywords
stop-signal task; cognitive control; frontal eye field; cognitive modeling; stochastic decision models
Categories
Funding
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [P30HD015052] Funding Source: NIH RePORTER
- NATIONAL EYE INSTITUTE [P30EY008126, F32EY016679, R01EY013358] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH055806] Funding Source: NIH RePORTER
- NEI NIH HHS [P30-EY08126, R01-EY13358, F32-EY016679] Funding Source: Medline
- NICHD NIH HHS [P30-HD015052] Funding Source: Medline
- NIMH NIH HHS [R01-MH55806] Funding Source: Medline
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The stop-signal task has been used to study normal cognitive control and clinical dysfunction. Its utility is derived from a race model that accounts for performance and provides an estimate of the time it takes to stop a movement. This model posits a race between go and stop processes with stochastically independent finish times. However, neurophysiological studies demonstrate that the neural correlates of the go and stop processes produce movements through a network of interacting neurons. The juxtaposition of the computational model with the neural data exposes a paradox-how can a network of interacting units produce behavior that appears to be the outcome of an independent race? The authors report how a simple, competitive network can solve this paradox and provide an account of what is measured by stop-signal reaction time.
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