There is consensus that activation within distributed functional brain networks underlies human thought. The impact of this consensus is limited, however, by a gap that exists between data-driven correlational analyses that specify where functional brain activity is localized using functional magnetic resonance imaging (fMRI), and neural process accounts that specify how neural activity unfolds through time to give rise to behavior. Here, we show how an integrative cognitive neuroscience approach may bridge this gap. In an exemplary study of visual working memory, we use multilevel Bayesian statistics to demonstrate that a neural dynamic model simultaneously explains behavioral data and predicts localized patterns of brain activity, outperforming standard analytic approaches to fMRI. The model explains performance on both correct trials and incorrect trials where errors in change detection emerge from neural fluctuations amplified by neural interaction. Critically, predictions of the model run counter to cognitive theories of the origin of errors in change detection. Results reveal neural patterns predicted by the model within regions of the dorsal attention network that have been the focus of much debate. The model-based analysis suggests that key areas in the dorsal attention network such as the intraparietal sulcus play a central role in change detection rather than working memory maintenance, counter to previous interpretations of fMRI studies. More generally, the integrative cognitive neuroscience approach used here establishes a framework for directly testing theories of cognitive and brain function using the combined power of behavioral and fMRI data. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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神经过程如何引起认知？通过视觉工作记忆的动态模型同时预测大脑和行为。

人们普遍认为，分布式功能性大脑网络内的激活是人类思想的基础。但是，这种共识的影响受到数据驱动的相关分析之间的差距的限制，该分析指定了使用功能磁共振成像（fMRI）定位功能性大脑活动的位置，而神经过程帐户则指定了神经活动如何随时间变化引起行为。在这里，我们展示了整合的认知神经科学方法如何弥合这一差距。在视觉工作记忆的一项示例性研究中，我们使用多级贝叶斯统计数据来证明神经动力学模型可以同时解释行为数据并预测大脑活动的局部模式，胜过功能磁共振成像的标准分析方法。该模型解释了正确试验和不正确试验的性能，在这些试验中，由于通过神经交互作用放大的神经波动会产生变化检测错误。至关重要的是，模型的预测与变更检测中错误源的认知理论背道而驰。结果揭示了模型在背侧注意力网络区域内所预测的神经模式，这已成为许多争论的焦点。基于模型的分析表明，背注意力网络中的关键区域（如顶内沟）在变化检测中起着中心作用，而不是在工作记忆维持中起着重要作用，这与fMRI研究以前的解释相反。更普遍，这里使用的综合认知神经科学方法建立了一个框架，用于使用行为和fMRI数据的组合能力直接测试认知和脑功能理论。（PsycInfo数据库记录（c）2021 APA，保留所有权利）。