We employ a reverse-engineering approach to illuminate the neurocomputational building blocks that combine to support controlled semantic cognition: the storage and context-appropriate use of conceptual knowledge. By systematically varying the structure of a computational model and assessing the functional consequences, we identified the architectural properties that best promote some core functions of the semantic system. Semantic cognition presents a challenging test case, as the brain must achieve two seemingly contradictory functions: abstracting context-invariant conceptual representations across time and modalities, while producing specific context-sensitive behaviours appropriate for the immediate task. These functions were best achieved in models possessing a single, deep multimodal hub with sparse connections from modality-specific regions, and control systems acting on peripheral rather than deep network layers. The reverse-engineered model provides a unifying account of core findings in the cognitive neuroscience of controlled semantic cognition, including evidence from anatomy, neuropsychology and functional brain imaging.

我们采用逆向工程方法来阐明结合支持受控语义认知的神经计算构建块：概念知识的存储和上下文适当使用。通过系统地改变计算模型的结构并评估功能结果，我们确定了最能促进语义系统某些核心功能的架构属性。语义认知提出了一个具有挑战性的测试案例，因为大脑必须实现两个看似矛盾的功能：跨时间和模式抽象上下文不变的概念表示，同时产生适合当前任务的特定上下文敏感行为。这些功能最好在具有单一的模型中实现，具有来自特定模式区域的稀疏连接的深度多模式集线器，以及作用于外围而非深层网络层的控制系统。逆向工程模型提供了对受控语义认知的认知神经科学核心发现的统一说明，包括来自解剖学、神经心理学和功能性脑成像的证据。