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Inhibitory control in mind and brain: An interactive race model of countermanding saccades.
Abstract
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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“Boucher et al. [64] proposed an interactive race model, in which the go and stop process are independent for much of their latencies and interact strongly near the end (Figure III).”
From Response inhibition in the stop-signal paradigm (2008) · cites this paper as [64]
In the citing paper
“In this model, SSRT primarily reflects the period before the stop unit is activated, during which stop and go processing are independent, so its predictions correspond to those of the independent race model [64].”
From Response inhibition in the stop-signal paradigm (2008) · cites this paper as [64]
In the citing paper
“In situations when there is a conflict, for instance when the subject has to inhibit a planned saccade by a stop signal introduced with a short delay after the target onset, the behavior becomes probabilistic (the saccade is suppressed on some trials, but not on other trials) (Logan and Cowan, 1984; Boucher et al., 2007).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Boucher et al., 2007
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Metadata
- Authors
- Boucher, L., Palmeri, T. J., Logan, G. D., & Schall, J. D.
- Affiliations
- Vanderbilt University (US)
- Venue
- Psychological Review
- Published
- 2007
- Volume
- 114
- Issue
- 2
- Pages
- 376-397
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2150380514
- Cited by
- 485
- References
- 165
- Citations by year
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- FWCI
- 13.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- NSUWorks (Nova Southeastern University) (submittedVersion)
- Funders
- E. Bronson Ingram Chair
- National Science Foundation (BCS0218507; BCS0446806)
- National Institutes of Health (F32-EY016679; RO1-MH55806; RO1-EY13358; P30-EY08126; P30-HD015052)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- inhibitory control, cognitive control, stop-signal task, race model, stop-signal reaction time, competitive neural networks, interacting neurons
- MeSH terms
- Brain, Cognition, Humans, Inhibition, Psychological, Saccades, Stochastic Processes, Signal Detection, Psychological
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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