The Goal Circuit Model: A Hierarchical Multi-Route Model of the Acquisition and Control of Routine Sequential Action in Humans.

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Title: The Goal Circuit Model: A Hierarchical Multi-Route Model of the Acquisition and Control of Routine Sequential Action in Humans.
Authors: Cooper, Richard P.1, Ruh, Nicolas1, Mareschal, Denis1
Source: Cognitive Science. Mar2014, Vol. 38 Issue 2, p244-274. 31p.
Subject Terms: *Goal (Psychology), *Cognitive ability, Sequential analysis, Case-control method, Task analysis
Abstract: Human control of action in routine situations involves a flexible interplay between (a) task-dependent serial ordering constraints; (b) top-down, or intentional, control processes; and (c) bottom-up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top-down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal-directed action that goes beyond existing models by operationalizing an interface between two putative systems-a routine and a non-routine system-thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non-routine goal-based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system. [ABSTRACT FROM AUTHOR]
Copyright of Cognitive Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: *<searchLink fieldCode="DE" term="%22Goal+%28Psychology%29%22">Goal (Psychology)</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+analysis%22">Sequential analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Case-control+method%22">Case-control method</searchLink><br /><searchLink fieldCode="DE" term="%22Task+analysis%22">Task analysis</searchLink>
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  Data: Human control of action in routine situations involves a flexible interplay between (a) task-dependent serial ordering constraints; (b) top-down, or intentional, control processes; and (c) bottom-up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top-down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal-directed action that goes beyond existing models by operationalizing an interface between two putative systems-a routine and a non-routine system-thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non-routine goal-based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Cognitive Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Mar2014
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