Inferring a Cognitive Architecture from Multitask Neuroimaging Data: A Data‐Driven Test of the Common Model of Cognition Using Granger Causality
Open accessAbstract
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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The common model of cognition is both a theoretical description of human memory systems and a description of real computational systems used to implement intelligent agents.
“The common model of cognition (Laird, Lebiere, and Rosenbloom 2017) is both a theoretical description of the memory systems that underlie human cognition (Hake, Sibert, and Stocco 2022) and a description of real computational systems (cognitive architectures) used to implement intelligent agents.”
From Memory Matters: The Need to Improve Long-Term Memory in LLM-Agents (2024) · cites this paper as Hake, Sibert, and Stocco 2022
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Memory Matters: The Need to Improve Long-Term Memory in LLM-Agents
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“Despite some pioneering work on data-driven cognitive model creation (Hake, Sibert, and Stocco 2022), almost all previous work generate their initial set of production rules manually, limiting their application to simple environments such as blocks world or psychology experiments (Park et al. 2023).”
BackgroundSibert, C., Hake, H. S., & Stocco, A. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.832503 · Cited by 6 · see citing works
“As a test of convergent validity of the CMC, a data-driven approach was implemented (Hake et al., 2022) in an effort to derive potential network structure directly from brain activity.”
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“Second, the same architecture emerges when the original time-series data from each region is analyzed using Granger causality [33].”
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Self-citationToward Human-Like Representation Learning for Cognitive Architectures
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“Consider that Perception and Action representations may feed directly to Long-Term Memory (Hake, Sibert, and Stocco 2022).”
Background
- Authors
- Hake, H. S., Sibert, C., & Stocco, A.
- Affiliations
- University of Washington (US)
- University of Groningen (NL)
- Corresponding author
- Sibert, Catherine
- Venue
- Topics in Cognitive Science
- Publisher
- Wiley
- Published
- 2022-09-21
- Volume
- 14
- Issue
- 4
- Pages
- 845-859
- ISSN
- 1756-8757 (ISSN-L)
- 1756-8765
- OpenAlex
- W4296706399
- Semantic Scholar
- dffea8c75ee90e5eae1c3873fca28112a9d1a883
- Cited by
- 8
- References
- 37
- Citations by year
- 2025: 2 · 2024: 4 · 2022: 2
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Topics in Cognitive Science (publishedVersion, other-oa)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- Air Force Office of Scientific Research (FA9550-, FA9550-19-1-0299)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- cognitive architecture, Granger causality, functional connectivity, episodic memory, theory of cognition, computational modeling of cognition
- MeSH terms
- Brain, Cognition, Humans, Magnetic Resonance Imaging, Nerve Net, Causality, Neuroimaging
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