Max-Planck-Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
Donders Institute for Brain, Cognition, and Behaviour, Radboud University and Radboud University Medical Center, 6525 EN Nijmegen, The Netherlands.
J Neurosci. 2021 Sep 8;41(36):7675-7686. doi: 10.1523/JNEUROSCI.0657-21.2021. Epub 2021 Jul 30.
A key aspect of conceptual knowledge is that it can be flexibly applied at different levels of abstraction, implying a hierarchical organization. It is yet unclear how this hierarchical structure is acquired and represented in the brain. Here we investigate the computations underlying the acquisition and representation of the hierarchical structure of conceptual knowledge in the hippocampal-prefrontal system of 32 human participants (22 females). We assessed the hierarchical nature of learning during a novel tree-like categorization task via computational model comparisons. The winning model allowed to extract and quantify estimates for accumulation and updating of hierarchical compared with single-feature-based concepts from behavior. We find that mPFC tracks accumulation of hierarchical conceptual knowledge over time, and mPFC and hippocampus both support trial-to-trial updating. As a function of those learning parameters, mPFC and hippocampus further show connectivity changes to rostro-lateral PFC, which ultimately represented the hierarchical structure of the concept in the final stages of learning. Our results suggest that mPFC and hippocampus support the integration of accumulated evidence and instantaneous updates into hierarchical concept representations in rostro-lateral PFC. A hallmark of human cognition is the flexible use of conceptual knowledge at different levels of abstraction, ranging from a coarse category level to a fine-grained subcategory level. While previous work probed the representational geometry of long-term category knowledge, it is unclear how this hierarchical structure inherent to conceptual knowledge is acquired and represented. By combining a novel hierarchical concept learning task with computational modeling of categorization behavior and concurrent fMRI, we differentiate the roles of key concept learning regions in hippocampus and PFC in learning computations and the representation of a hierarchical category structure.
概念知识的一个关键方面是它可以在不同的抽象层次上灵活应用，这意味着存在一种层次结构。目前尚不清楚这种层次结构是如何在大脑中获得和表示的。在这里，我们研究了在 32 名人类参与者（22 名女性）的海马-前额叶系统中，概念知识的层次结构的获得和表示所涉及的计算。我们通过计算模型比较评估了在新颖的树状分类任务中学习的层次性质。获胜模型允许从行为中提取和量化基于层次的概念与基于单一特征的概念的积累和更新的估计。我们发现 mPFC 随着时间的推移追踪层次概念知识的积累，mPFC 和海马体都支持基于试次的更新。作为这些学习参数的函数，mPFC 和海马体进一步显示与额侧前皮质的连接变化，这最终在学习的最后阶段代表了概念的层次结构。我们的结果表明，mPFC 和海马体支持将积累的证据和即时更新整合到额侧前皮质的层次概念表示中。人类认知的一个标志是在不同的抽象层次上灵活使用概念知识，从粗分类级别到细分类级别。虽然以前的工作探究了长期类别知识的表示几何形状，但尚不清楚概念知识固有的这种层次结构是如何获得和表示的。通过将新颖的层次概念学习任务与分类行为的计算建模和并发 fMRI 相结合，我们区分了海马体和 PFC 在学习计算和层次类别结构表示中的关键概念学习区域的作用。
