Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach

Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processi...

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Veröffentlicht in:Psychonomic bulletin & review Jg. 25; H. 1; S. 302 - 321
Hauptverfasser: Shahnazian, Danesh, Holroyd, Clay B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.02.2018
Springer Nature B.V
Schlagworte:
Animals
Behavioral Science and Psychology
Cognition & reasoning
Cognitive Psychology
Decision making
Error correction & detection
Goals
Gyrus Cinguli - physiology
Humans
Medical imaging
Memory
Models, Neurological
Motivation
Neural networks
Neural Networks (Computer)
Neurons
Neurons - physiology
Neurosciences
Psychology
Theoretical Review
Surprise
Anterior cingulate cortex
Recurrent neural network
Sequence learning
ISSN:1069-9384, 1531-5320, 1531-5320
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Zusammenfassung:Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.
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ISSN:1069-9384
1531-5320
1531-5320
DOI:10.3758/s13423-017-1280-1