The emergence of abstract structure knowledge across learning and sleep

学习和睡眠中抽象结构知识的出现

• 批准号: 10687207
• 负责人: Anna C Schapiro
• 金额: $ 20.31万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10687207
• 关键词: Area; Categories; Cognition; Cognitive; Cognitive deficits; Computer Models; Deterioration; Development; Disease; Electroencephalography; Elements; Environment; Exposure to; Graph; Hippocampus; Human; Individual; Investigation; Knowledge; Learning; Link; Literature; Maps; Memory; Memory impairment; Mental Depression; Mental disorders; Methods; Modeling; Nature; Neural Network Simulation; Participant; Pattern; Persons; Process; Rest; Role; Schizophrenia; Semantics; Sleep; Structure; Techniques; Testing; Visit; Wing; Work; awake; cognitive process; design; experience; experimental study; fly; improved; insight; nervous system disorder; neuroimaging; neuromechanism; novel; prototype; scaffold; social relationships; theories

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.

在任何给定的认知域中，单个元素的表征都不是独立的，而是由以下因素组织起来的： 结构化的关系。这种底层结构的表示非常强大，因为它们可以允许 在新的环境中进行归纳和推理。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），并被称为影响 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 新的类别，包含集群的可靠共同出现的功能，揭示了结构的影响， 新类别的形成。然而，一个关键的未知数是，学习的结构表征是否与 与特定类别的元素联系在一起，或者它们是否在某种程度上变得抽象， 经验的特点。此外，如果抽象的结构表示确实出现，先前的工作提供了有趣的 提示他们可能需要在清醒休息或睡眠期间进行离线整合。我们已经发展出一种模式， 精心设计的图形结构控制着个体内部的特征共现模式， 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便于学习由新的结构化类别定义的第二相同结构化类别。 一组特征。这种促进将提供证据表明，结构表征是抽象的一些 ℃下目标1将使用这些方法来评估是否出现了抽象的结构表示 在学习的同时。目标2将决定这些表征是持续存在，还是在一段延迟后出现， 以及是否特别需要基于睡眠的巩固。最近经验的重演在 睡眠将使用脑电图（EEG）与闭环目标记忆配对进行评估 重新激活（TMR），一种技术，使因果关系的影响，巩固最近学到的 人类的信息。这项工作将通知和约束语义学习的理论，以及理论， 更广泛地构建学习和表征。