The emergence of abstract structure knowledge across learning and sleep

在任何给定的认知域中，各个元素的表示不是独立的，而是由 结构化关系的手段。此基础结构的表示形式功能强大，因为它们可以 在新环境中的概括和推理。在语义域中，结构捕获关联 在不同的语义特征或概念之间(例如，绿色、翅膀、可以飞行)，并已知会影响 语义知识的发展和退化。我们最近发现，人类发现更容易学习 包含可靠共现特征的簇的新颖类别，揭示了结构对 新奇的品类构成。然而，一个关键的未知数是，习得的结构表示法是否紧密 绑定到类别特定的元素，或者它们是否在某种程度上变得抽象，从 经验丰富的功能。此外，如果抽象的结构表示确实出现了，先前的工作提供了有趣的 暗示他们可能需要在清醒、休息或睡眠期间进行离线整合。我们已经开发了一个范例， 哪些精心设计的图表结构控制着个体内要素共现的模式 类别。在这里，我们实现了这个范例的“结构转移”扩展，以确定是否 学习一个结构化类别有助于学习由新的 一组功能。这种便利将提供证据，证明结构表示对某些人来说是抽象的 学位。目标1将使用这些方法来评估是否出现抽象结构表征 在学习过程中立即进行。目标2将决定这些表述是持续存在，还是出现延迟， 以及是否特别需要以睡眠为基础的巩固。重播最近的经验在 睡眠评估将使用脑电(EEG)和闭环靶向记忆相结合的方法 重新激活(TMR)，这是一种对最近学到的巩固产生因果影响的技术 人类身上的信息。这项工作将启发和约束语义学习的理论以及语义学习的理论 更广泛地构建学习和表征的结构。

In any given cognitive domain, representations of individual elements are not independent but are organized by means of structured relations. Representations of this underlying structure are powerful because they can allow generalization and inference in novel environments. In the semantic domain, structure captures associations between different semantic features or concepts (e.g., green, wings, can fly) and is known to influence the development and deterioration of semantic knowledge. We recently found that humans find it easier to learn novel categories that contain clusters of reliably co-occurring features, revealing an influence of structure on novel category formation. However, a critical unknown is whether learned representations of structure are closely tied to category-specific elements, or whether they become abstract to some extent, transformed away from the experienced features. Further, if abstract structural representations do emerge, prior work provides intriguing hints that they may require offline consolidation during awake rest or sleep. We have developed a paradigm in which carefully designed graph structures govern the pattern of feature co-occurrences within individual categories. Here we implement a "structure transfer'' extension of this paradigm in order to determine whether learning one structured category facilitates learning of a second identically structured category defined by a new set of features. This facilitation would provide evidence that structure representations are abstract to some degree. Aim 1 will use these methods to evaluate whether abstract structural representations emerge immediately during learning. Aim 2 will determine whether these representations persist, or emerge, over a delay, and whether sleep-based consolidation in particular is needed. The role of replay of recent experience during sleep will be evaluated using electroencephalography (EEG) paired with closed-loop targeted memory reactivation (TMR), a technique that enables causal influence over the consolidation of recently learned information in humans. This work will inform and constrain theories of semantic learning as well as theories of structure learning and representation more broadly.