Human cognition flexibly guides decision-making in familiar and novel situations. Although these decisions are often treated as dichotomous, in reality, situations are neither completely familiar, nor entirely new. Contemporary accounts of brain organization suggest that neural function is organized along a connectivity gradient from unimodal regions of sensorimotor cortex, through executive regions to transmodal default mode network. We examined whether this graded view of neural organization helps to explain how decision-making changes across situations that vary in their alignment with long-term knowledge. We used a semantic judgment task, which parametrically varied the global semantic similarity of items within a feature matching task to create a ‘task gradient’, from conceptual combinations that were highly overlapping in long-term memory to trials that only shared the goal-relevant feature. We found the brain’s response to the task gradient varied systematically along the connectivity gradient, with the strongest response in default mode network when the probe and target items were highly overlapping conceptually. This graded functional change was seen in multiple brain regions and within individual brains, and was not readily explained by task difficulty. Moreover, the gradient captured the spatial layout of networks involved in semantic processing, providing an organizational principle for controlled semantic cognition across the cortex. In this way, the cortex is organized to support semantic decision-making in both highly familiar and less familiar situations.

中文翻译：

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从长期记忆到新颖认知的梯度：通过默认模式和执行皮层的转变


人类认知在熟悉和新奇的情况下灵活地指导决策。尽管这些决定通常被视为二分法，但实际上，情况既不是完全熟悉的，也不是全新的。现代对大脑组织的描述表明，神经功能是沿着从感觉运动皮层的单峰区域、通过执行区域到跨模式默认模式网络的连接梯度来组织的。我们研究了这种神经组织的分级观点是否有助于解释决策如何在与长期知识的一致性不同的情况下发生变化。我们使用语义判断任务，该任务以参数方式改变特征匹配任务中项目的全局语义相似性，以创建“任务梯度”，从长期记忆中高度重叠的概念组合到仅共享目标相关的试验特征。我们发现大脑对任务梯度的反应沿着连接梯度系统地变化，当探测和目标项目在概念上高度重叠时，默认模式网络中的反应最强。这种分级的功能变化出现在多个大脑区域和单个大脑内，并且很难用任务难度来解释。此外，梯度捕获了语义处理中涉及的网络的空间布局，为整个皮层的受控语义认知提供了组织原则。通过这种方式，大脑皮层的组织方式可以在高度熟悉和不太熟悉的情况下支持语义决策。