Department of Psychology, University of Washington, Seattle, WA 98195, United States.
Department of Psychology, University of Washington, Seattle, WA 98195, United States.
Neuroimage. 2021 Jul 15;235:118035. doi: 10.1016/j.neuroimage.2021.118035. Epub 2021 Apr 7.
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 a 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 six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three 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. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.
通用认知模型（CMC）是最近提出的一种共识架构，旨在捕捉认知科学几十年来在建模人类和类人智能方面的进展。由于其广泛的共识以及其组件与特定大脑区域的初步映射，我们假设 CMC 可以作为人类大脑大规模功能架构的候选模型。为了验证这一假设，我们分析了来自 200 名参与者和七个不同任务的功能磁共振成像数据，这些任务涵盖了广泛的认知领域。通过功能磁共振成像分析，确定了与 CMC 组件具有同功同源的大脑区域，并将它们的通信途径转化为区域之间预测的有效连接模式。然后使用动态因果建模来实现和拟合这个动态线性模型，并使用贝叶斯方法与神经科学领域之前提出的六种替代大脑架构（三种分层架构和三种枢纽与辐条架构）进行比较。结果表明，在所有情况下，CMC 在每个领域和所有任务中都远远优于所有其他架构。这些发现表明，一套用于人工智能的通用架构原则也为多个认知领域的人类大脑功能提供了基础。
