Testing the role of learned regularities in visual working memory: The nature of chunking for continuous visual features

测试学习规律在视觉工作记忆中的作用：连续视觉特征的组块本质

• 批准号: 2141189
• 负责人: Timothy Brady
• 金额: $ 52.39万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141189&HistoricalAwards=false
• 关键词: Testing; role; learned; regularities; visual

Working memory allows people to hold information in mind over a short period of time, and to work with this information. For example, while doing a math problem, working memory is used to hold in mind the numbers and manipulate them; and while driving, working memory is used to hold in mind the locations of other cars as you check whether it is safe to change lanes. Working memory has a severely limited capacity, such that only a few items can be accurately held in mind at any given time, and this limited capacity provides a strong constraint on people’s ability to perform many cognitive tasks. One well-known aspect of working memory capacity is that it is affected by learning: “chunking” frequently encountered information into compressed, meaningful units allows more efficient representations, and allows us to store more in working memory. For example, remembering a random string of 7 numbers is much more cognitively demanding than remembering our own phone number. The current research investigates the cognitive and neural basis of how learning increases the amount of information that we can process via chunking, and the extent to which the contents of “chunks” are actively stored in working memory vs. off-loaded into long-term memory after we have learned them. Importantly, the amount of information a person is able to hold in mind in working memory has been shown to be related to many other cognitive abilities (including intelligence), and disruptions of working memory are common in clinical disorders like attention deficit hyperactivity disorder and schizophrenia. Therefore, new knowledge regarding how this information is stored and used is critical for understanding the nature of important differences between individuals and how such differences may be impacted by learning. In addition, in many real-world situations it is vital to maintain detail about many objects or events at once, to make informed decisions in real time (e.g., changing lanes while driving). This research will inform how learning affects working memory capacity in such situations. In addition to this work on chunking, the proposal also includes the development of tools to allow scientists to measure memory performance and to do so in a wider variety of individuals (i.e., via internet-based experiments), as well as an outreach component, featuring the training and recruitment of students from underrepresented groups.The research uses behavioral and electrophysiological measures of working memory to test the role of item-specific, detailed information in chunk learning. The proposed studies are meant to distinguish between two potential theoretical explanations for how chunks are instantiated in working memory, one based on content-free pointers and the other based on hierarchical storage of objects and their features. A sequence of behavioral experiments asks how people’s representation of the visual features of objects is affected by learning, and in particular the extent to which learned regularities cause people to lose access to the details of visual objects in working memory. These behavioral experiments are followed by replications and extensions using electroencephalography (EEG), which will be analyzed to look at an event-related potential, the contralateral delay activity, which provides a measure of the amount of information held in working memory. Together, the behavioral and electrophysiological measures inform how learning and chunking affect our capacity to hold information in mind in working memory.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

工作记忆允许人们在短时间内记住信息，并利用这些信息进行工作。例如，在做数学题时，工作记忆被用来记住数字并操纵它们;在开车时，工作记忆被用来记住其他汽车的位置，因为你检查是否可以安全地改变车道。工作记忆的容量非常有限，在任何给定的时间里，只有少数几个项目可以准确地记住，这种有限的容量对人们执行许多认知任务的能力提供了很大的限制。工作记忆容量的一个众所周知的方面是，它受到学习的影响：将经常遇到的信息“分块”成压缩的、有意义的单元，可以更有效地表示，并允许我们在工作记忆中存储更多。例如，记住一串随机的7个数字比记住我们自己的电话号码更需要认知。目前的研究调查了学习如何增加我们可以通过组块处理的信息量的认知和神经基础，以及“组块”的内容在多大程度上被积极地存储在工作记忆中，而不是在我们学习后卸载到长期记忆中。重要的是，一个人能够在工作记忆中记住的信息量已经被证明与许多其他认知能力（包括智力）有关，并且工作记忆的中断在注意缺陷多动障碍和精神分裂症等临床疾病中很常见。因此，关于如何存储和使用这些信息的新知识对于理解个体之间重要差异的性质以及学习如何影响这些差异至关重要。此外，在许多真实世界的情况下，同时保持关于许多对象或事件的细节、在真实的时间内做出明智的决定（例如，在驾驶时改变车道）。这项研究将揭示在这种情况下学习如何影响工作记忆容量。除了这项关于组块的工作外，该提案还包括开发工具，使科学家能够测量记忆性能，并在更广泛的个体中进行测量（即，通过基于互联网的实验），以及一个外展部分，其特点是培训和招聘学生从代表性不足的群体。该研究使用行为和电生理措施的工作记忆测试的作用，具体项目，详细的信息在组块学习。所提出的研究是为了区分两个潜在的理论解释块是如何在工作记忆中实例化，一个基于内容自由指针和其他基于对象及其功能的分层存储。一系列行为实验询问人们对物体视觉特征的表征如何受到学习的影响，特别是学习障碍导致人们在工作记忆中失去对视觉物体细节的访问的程度。在这些行为实验之后，使用脑电图（EEG）进行复制和扩展，分析EEG以观察事件相关电位，即对侧延迟活动，它提供了对工作记忆中信息量的测量。行为和电生理测量一起告知学习和组块如何影响我们在工作记忆中记住信息的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。