Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for abstract symbols (e.g., democracy). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of abstract concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and abstract meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, abstract concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms.

中文翻译：

---

语言符号支持具体概念，但也能形成抽象概念：来自大脑受限深度神经网络的证据


具体的符号（例如，太阳、奔跑）可以在物体和动作的背景下学习，从而将它们的意义扎根于世界。然而，对于抽象符号（例如民主）是否存在类似的语义学习途径存在争议。当我们使用大脑约束的深度神经网络模拟概念/语义基础的假定大脑机制时，学习语言上下文之外的具体概念实例会导致强大的神经回路产生大量且持久的激活。相比之下，抽象概念实例的学习产生的活动大大减少，而且只是短暂的。至关重要的是，当在单词形式的背景下学习概念实例时，电路激活对于具体和抽象意义都变得强大且持久。这些结果表明，虽然具体概念表征的神经关联可以仅从基础经验中建立，但神经生物学层面的抽象概念形成是由语言形式的相关存在实现的，并且需要语言形式的相关存在。