Article ; Online: Inferring a Cognitive Architecture from Multitask Neuroimaging Data: A Data-Driven Test of the Common Model of Cognition Using Granger Causality.
2022 Volume 14, Issue 4, Page(s) 845–859
Abstract: Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high-level cognitive architecture, known ... ...
Abstract | Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high-level cognitive architecture, known as the "Common Model of Cognition," to task-based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top-down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this paper, we propose a bottom-up method to infer a cognitive architecture directly from brain imaging data itself, overcoming this limitation. Specifically, Granger causality modeling was applied to the same task-based fMRI data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model of Cognition but also suggests important additions likely related to the role of episodic memory. This combined top-down and bottom-up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition. |
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MeSH term(s) | Humans ; Neuroimaging ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Cognition ; Causality ; Nerve Net |
Language | English |
Publishing date | 2022-09-21 |
Publishing country | United States |
Document type | Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. |
ZDB-ID | 2482883-X |
ISSN | 1756-8765 ; 1756-8757 |
ISSN (online) | 1756-8765 |
ISSN | 1756-8757 |
DOI | 10.1111/tops.12623 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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