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Refining the Common Model of Cognition Through Large Neuroscience Data
Author(s) -
Zoe Steine-Hanson,
Natalie Koh,
Andrea Stocco
Publication year - 2018
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2018.11.026
Subject(s) - human connectome project , generalizability theory , computer science , cognition , empirical evidence , empirical research , cognitive science , cognitive psychology , artificial intelligence , functional connectivity , psychology , neuroscience , epistemology , developmental psychology , philosophy
The Common Model of Cognition (CMC) is an effort to highlight the commonalities between multiple studies of human-like intelligent minds and cognition, and bring these architectures and functions together into one common model. The CMC is still relatively new, and while it has substantial theoretical evidence in its favor, there is little empirical evidence confirming the theory. The CMC is fundamentally informed by human cognition, and must at least hold true for human brains. As such, this paper uses a large fMRI dataset from the Human Connectome Project (HCP) to refine the CMC and test it as a reasonable model for human cognition. We tested three models in the CMC family and two alternative models against two tasks from the HCP dataset. We found that one model from the CMC family explained the HCP dataset best, providing further empirical evidence in favor of the CMC, whilst also suggesting a slight modification to the CMC itself that may improve the model’s generalizability.

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