{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T04:55:02Z","timestamp":1692766502141},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Topics in Cognitive Science"],"published-print":{"date-parts":[[2022,10]]},"abstract":"Abstract<\/jats:title>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\u2010level cognitive architecture, known as the \u201cCommon Model of Cognition,\u201d to task\u2010based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top\u2010down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this paper, we propose a bottom\u2010up 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\u2010based 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\u2010down and bottom\u2010up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition.<\/jats:p>","DOI":"10.1111\/tops.12623","type":"journal-article","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T17:24:30Z","timestamp":1663781070000},"page":"845-859","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Inferring a Cognitive Architecture from Multitask Neuroimaging Data: A Data\u2010Driven Test of the Common Model of Cognition Using Granger Causality"],"prefix":"10.1111","volume":"14","author":[{"given":"Holly Sue","family":"Hake","sequence":"first","affiliation":[{"name":"Department of Psychology and Neuroscience Program, University of Washington, Seattle"}]},{"given":"Catherine","family":"Sibert","sequence":"additional","affiliation":[{"name":"Bernoulli Institute University of Groningen"}]},{"given":"Andrea","family":"Stocco","sequence":"additional","affiliation":[{"name":"Department of Psychology and Neuroscience Program, University of Washington, Seattle"}]}],"member":"311","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195324259.001.0001"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2008.01.006"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.033"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0536-12.2012"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.20606"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1225266"},{"key":"e_1_2_8_8_1","volume-title":"Statistical methods for research workers","author":"Fisher R. 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