ORA (Round 5): Hidden brain states underlying efficient representations in working memory

ORA（第 5 轮）：工作记忆中有效表征的隐藏大脑状态

• 批准号: ES/S015477/1
• 负责人: Mark Stokes
• 金额: $ 56.79万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FS015477%2F1
• 关键词: ORA; Round; Hidden; brain; states

Human working memory not only serves to keep track of changes around us, but is also vital to our ability to learn. When it comes to the nature of working memory, two facts are beyond doubt: First, it is notoriously limited and can hold only a handful of items at any given time. Second, this precious limited resource needs to be optimised, by organising memoranda into structured representations of objects and events.Beyond these basic facts, however, the nature of storage space in working memory is not well defined and is currently heavily debated. Even advanced measures of brain activity have so far not been able to provide decisive evidence. Fundamental challenges in measuring the neural correlates of working memory impede a better understanding of how memory limits arise and how they might be alleviated.Past research has assumed working memory is represented in persistent neural activity; however, we argue that neural coding for working memory actually goes far deeper than such static activity states. Until recently, there was no obvious method to explore working memory representations beyond persistent delay activity, but a new technique ('neural sonar') we developed allows a fresh perspective on the underlying, hidden states of the neural network. We will apply our new approach to determine the fundamental nature of capacity limits in working memory, and how encoding, maintenance and working memory-guided decision making can be optimised for superior memory performance.

人类的工作记忆不仅可以跟踪我们周围的变化，而且对我们的学习能力也至关重要。谈到工作记忆的本质，有两个事实是毋庸置疑的：第一，它是出了名的有限，在任何给定的时间只能容纳少数几个项目。第二，需要优化这一宝贵的有限资源，将备忘录组织成对象和事件的结构化表征。然而，除了这些基本事实之外，工作记忆中存储空间的性质并没有得到很好的定义，目前存在大量争议。到目前为止，即使是先进的大脑活动测量方法也无法提供决定性的证据。测量工作记忆的神经相关性的基本挑战阻碍了更好地理解记忆限制是如何产生的，以及如何缓解的。过去的研究假设工作记忆是在持续的神经活动中表现出来的;然而，我们认为，工作记忆的神经编码实际上比这种静态活动状态要深得多。直到最近，除了持续延迟活动之外，还没有明显的方法来探索工作记忆表征，但是我们开发的一种新技术（“神经声纳”）允许对神经网络的潜在隐藏状态进行新的视角。我们将应用我们的新方法来确定工作记忆容量限制的基本性质，以及如何优化编码，维护和工作记忆引导的决策，以获得上级记忆性能。

项目成果

Theoretical distinction between functional states in working memory and their corresponding neural states.

工作记忆中功能状态与其相应神经状态中的功能状态之间的理论区别。

• DOI: 10.1080/13506285.2020.1825141
• 发表时间: 2020-09-24
• 期刊: Visual cognition
• 影响因子: 2
• 作者: Stokes MG;Muhle-Karbe PS;Myers NE
• 通讯作者: Myers NE

Decoding visual colour from scalp electroencephalography measurements.

• DOI: 10.1016/j.neuroimage.2021.118030
• 发表时间: 2021-08-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Hajonides JE;Nobre AC;van Ede F;Stokes MG
• 通讯作者: Stokes MG

Drifting codes within a stable coding scheme for working memory

工作记忆稳定编码方案中的漂移代码

• DOI: 10.1101/714311
• 发表时间: 2019
• 作者: Wolff M

Theoretical distinction between functional states in working memory and their corresponding neural states

工作记忆功能状态与其相应神经状态之间的理论区别

• DOI: 10.31234/osf.io/n37su
• 发表时间: 2020
• 作者: Stokes M