Mechanisms underlying learning-related representational reorganization

学习相关表征重组的潜在机制

• 批准号: RGPIN-2020-05958
• 负责人: Wammes, Jeffrey
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718124
• 关键词: Mechanisms; underlying; learning; related; representational

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.

作为人类，我们拥有看似无限的新学习能力，但只有有限数量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。这种适应性的组织是至关重要的，可能是发展专业知识，不受干扰地记忆和概括经验的基础。在拟议的工作中，我们将结合联合收割机行为实验，神经成像和计算方法来表征记忆是如何适应性重组，因为我们在世界上学习和行动。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，我的目标是量化与学习相关的记忆重组如何影响后来的表现，开发防止干扰和促进学习的方法。下文提出的三个目标有助于实现这些更广泛的目标。 AIM1 当我们体验我们的视觉世界时，我们编码到记忆中的不是我们经验的快照，而是分层视觉处理轨迹的结果，其中低层次的方向和颜色被构建成对象身份，这些身份被前馈到我们的学习系统中。为了理解表征是如何在记忆中重组自己的，我们必须理解编码的这条路径。在AIM1中，我们利用计算机视觉的最新进展将模型特征映射到人类视觉皮层，这将使我们能够理解各种视觉复杂性水平的表征组织。 AIM2 在最近的试点研究中，我们证明了，当它们之间存在适度的重叠或竞争时，学习的表征彼此区分，或变得更加明显。相反，当存在高度重叠时，学习的表征彼此整合。在AIM2中，我们将探索这种与竞争相关的分化是一种跨任务和刺激类型发生的域一般学习机制的诱人可能性。 AIM3 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AIM 3中，我们将测试这样的假设：重叠绘画之间的竞争会驱动差异化，主要是最有特色的绘画会被记住，并且视觉运动连接的独特模式会导致记忆力的提高。 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的来推动该领域的发展。研究结果还将为改善教育提供潜在的应用方法。通过这项工作，我们还将开发和分享能够对大脑中的表征空间进行控制的尖端工具。这些发现及其应用潜力不仅会引起研究人员的兴趣，也会引起教育工作者和公众的兴趣。