Analysis of the Human Connectome Data Supports the Notion of A “Common Model of Cognition” for Human and Human-Like Intelligence (bibtex)
by Andrea Stocco, Zoe Steine-Hanson, Natalie Koh, John E. Laird, Christian J. Lebiere, Paul Rosenbloom
Abstract:
The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover the broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against four alternative brain architectures that had been previously proposed in the field of neuroscience (two hierarchical architectures and two hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. The results suggest that a common, general architecture that could be used for artificial intelligence effectively underpins all aspects of human cognition, from the overall functional architecture of the human brain to higher level thought processes.
Reference:
Analysis of the Human Connectome Data Supports the Notion of A “Common Model of Cognition” for Human and Human-Like Intelligence (Andrea Stocco, Zoe Steine-Hanson, Natalie Koh, John E. Laird, Christian J. Lebiere, Paul Rosenbloom), 2019.
Bibtex Entry:
@techreport{stocco_analysis_2019,
	type = {preprint},
	title = {Analysis of the {Human} {Connectome} {Data} {Supports} the {Notion} of {A} “{Common} {Model} of {Cognition}” for {Human} and {Human}-{Like} {Intelligence}},
	url = {http://biorxiv.org/lookup/doi/10.1101/703777},
	abstract = {The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover the broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against four alternative brain architectures that had been previously proposed in the field of neuroscience (two hierarchical architectures and two hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. The results suggest that a common, general architecture that could be used for artificial intelligence effectively underpins all aspects of human cognition, from the overall functional architecture of the human brain to higher level thought processes.},
	institution = {Neuroscience},
	author = {Stocco, Andrea and Steine-Hanson, Zoe and Koh, Natalie and Laird, John E. and Lebiere, Christian J. and Rosenbloom, Paul},
	month = jul,
	year = {2019},
	doi = {10.1101/703777},
	keywords = {Virtual Humans, UARC}
}
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