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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神经过程如何产生认知？使用视觉工作记忆的动态模型同时预测大脑和行为。

人们一致认为，分布式功能性大脑网络中的激活是人类思维的基础。然而，这一共识的影响有限，因为数据驱动的相关分析（使用功能性磁共振成像 (fMRI) 指定功能性大脑活动定位于何处）与指定神经活动如何随时间展开的神经过程帐户之间存在差距产生行为。在这里，我们展示了综合认知神经科学方法如何弥合这一差距。在视觉工作记忆的示例性研究中，我们使用多级贝叶斯统计来证明神经动态模型同时解释行为数据并预测大脑活动的局部模式，优于 fMRI 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，其中变化检测中的错误是由神经交互放大的神经波动引起的。至关重要的是，模型的预测与变化检测错误起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预测的神经模式，这一直是许多争论的焦点。基于模型的分析表明，与之前对 fMRI 研究的解释相反，背侧注意力网络中的关键区域，如顶内沟，在变化检测中发挥着核心作用，而不是工作记忆维持。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，可以使用行为和 fMRI 数据的综合力量直接测试认知和大脑功能的理论。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。