Mechanisms underlying learning-related representational reorganization

作为人类，我们拥有看似无限的新学习能力，但只有有限的神经组织来存储信息。我们的大脑通过部分重复使用神经元来表示新信息来实现这一点。这种适应性组织是至关重要的，可能是发展专业知识、不受干扰地记忆和概括经验的基础。在提议的工作中，我们将结合行为实验、神经成像和计算方法来描述记忆是如何在我们学习和行动的过程中适应性地重组的。我研究的长期目标是了解适应性学习和记忆系统的基本认知机制。此外，我的目标是量化学习相关的记忆重组如何影响后来的表现，开发方法来防止干扰和促进学习。下面提出的三个目标有助于实现这些更广泛的目标。ai1当我们体验视觉世界时，我们编码到记忆中的不是我们体验的快照，而是分层视觉处理轨迹的结果，其中低级的方向和颜色被构建到物体身份中，并被馈送到我们的学习系统中。要理解表示如何在内存中重新组织自己，我们必须理解这条编码路径。在AIM1中，我们利用计算机视觉的最新进展，从模型特征映射到人类视觉皮层，这将使我们能够理解不同层次视觉复杂性的表征组织。在最近的试点研究中，我们证明，当学习表征之间有适度的重叠或竞争时，它们彼此区分，或变得更加明显。相反，当有高度重叠时，学习到的表征相互整合。在AIM2中，我们将探讨这种与竞争相关的分化是一种跨任务和刺激类型的领域通用学习机制的诱人可能性。ai3在最近的工作中，我证明了绘画可以培养独特的记忆。绘画，通过创造一个上下文丰富的记忆痕迹，可能会使记忆更加独特。在AIM3中，我们将测试重叠图形之间的竞争驱动分化的假设，主要是最独特的图形被记住，以及视觉运动连接的独特模式导致记忆的改善。总之，这些研究目标的影响将通过阐明记忆表征是如何编码和重组来推动该领域向前发展。研究结果还将产生改善教育的潜在应用方法。通过这项工作，我们还将开发和分享能够控制大脑中实例化的表征空间的尖端工具。这些发现及其应用潜力不仅会引起研究人员的兴趣，而且也会引起教育工作者和一般公众的兴趣。

As humans, we boast a seemingly unbounded capacity for new learning, but have only a finite amount of neural tissue to store information. Our brains accomplish this by partially reusing neurons to represent new information. This adaptive organization is critical and may be fundamental to developing expertise, remembering without interference, and generalizing across experiences. In the proposed work, we will combine behavioural experiments, neuroimaging, and computational methods to characterize how memories are adaptively reorganized as we learn and act in the world. The long-term aim of my research is to understand the basic cognitive mechanisms underlying adaptive learning and memory systems. Furthermore, I aim to quantify how learning-related memory reorganization impacts later performance, developing approaches for preventing interference and promoting learning. The three aims presented below contribute to addressing these broader goals. AIM1 When we experience our visual world, what we encode into memory is not a snapshot of our experience, but rather the result of a trajectory of hierarchical visual processing, wherein low-level orientations and colors are built into object identities, which are fed forward into our learning systems. To understand how representations reorganize themselves in memory, we must understand this path to encoding. In AIM1, we leverage recent advances in computer vision to map from model features to the human visual cortex, which will allow us to understand representational organization at various levels of visual complexity. AIM2 In recent pilot research, we demonstrated that learned representations differentiate from one another, or become more distinct, when there is moderate overlap, or competition between them. In contrast, when there is high overlap, learned representations integrate with one another. In AIM2 we will explore the alluring possibility that such competition-related differentiation is a domain-general learning mechanism, that occurs across tasks and stimulus types. AIM3 In recent work, I demonstrated that drawing can foster distinctive memories. Drawing, through creating a context-rich memory trace, might render memories more distinctive. In AIM3, we will test the hypothesis that competition between overlapping drawings drives differentiation, that primarily the most distinctive drawings are remembered, and that distinctive patterns of visuomotor connectivity leads to improved memory. Together, the impact of these research aims will propel the field forward by illuminating how memory representations are encoded and reorganized. The results will also yield potential applied approaches for improving education. Through this work, we will also develop and share cutting-edge tools capable of exerting control over the representational spaces instantiated in the brain. The findings and their applied potential will be of interest no only to researchers, but also to educators, and the general public.