Learning and Compression in Human Working Memory

描述(由申请人提供)：学习和工作记忆的一个重要但相对未被探索的方面是，感知者如何以及在什么条件下能够利用环境中的规则来记忆更多信息。我们以前的经历如何影响我们记忆现在的经历？如何利用这种影响作为工具来增强学习障碍者(例如自闭症患者)的认知过程，或帮助记忆缺陷者？为了解决这些问题，这项建议利用了一种强大的内隐学习形式，即视觉统计学习。先前的研究表明，观察者对视觉环境中的规律性非常敏感(例如，很快就会知道“A”后面通常是“B”)。令人惊讶的是，这种学习往往是完全内隐的：当被要求明确报告这些规律时，观察者会偶然地做出反应。这意味着视觉统计学习是一个强大的过程，它在没有我们的意图或有意识的控制的情况下自动运行。然而，目前还不清楚视觉统计学习对记忆过程有什么好处。特别是，统计规则是冗余的一种形式，在人类记忆压缩冗余信息的程度上，对象之间的学习规则应该使观察者能够记住关于更多对象的信息。拟议的实验有三个目标：(1)确定视觉统计学习是否使观察者能够压缩信息并记住更多信息；(2)确定压缩的“单位”；(3)确定压缩发生的“水平”。为了调查这些问题，观察者将被要求记住简单的物体。随着时间的推移，一些观察者将看到输入中的模式(例如，‘A’经常与‘B’一起出现)，而其他观察者将看到随机输入。在一定程度上，观察者可以学习规律性，并将其压缩以形成更有效的记忆表征，图案组中的观察者应该记住更多物体的细节。以往的研究没有直接探讨视觉统计学习如何影响工作记忆的容量，所提出的研究将为学习和记忆之间的相互作用提供重要的见解。 与公共健康相关：这项提议的一个令人兴奋的方面是，它承诺通过研究统计学习如何影响工作记忆和认知来为基础科学提供信息，因此有可能增加对具有学习或记忆相关症状的精神疾病的理解。例如，自闭症患者有一定的学习缺陷，但尚不清楚自闭症患者的统计学习及其与工作记忆的相互作用是否也受到损害。拟议的研究为未来解决这些问题的临床转化性研究奠定了基础。

DESCRIPTION (provided by applicant): An important, yet relatively unexplored aspect of learning and working memory is how, and under what conditions, a perceiver can capitalize on regularities in the environment to remember more information. How can our prior experiences influence how we remember our current experiences? How can such influences be exploited as tools to enhance cognitive processes in those with learning disabilities (e.g., in Autism), or to assist those with memory deficits? To address these issues, this proposal makes use of a powerful form of implicit learning known as visual statistical learning. Previous research has shown that observers are extremely sensitive to regularities in the visual environment (e.g., quickly learning that 'A' is usually followed by 'B'). Surprisingly, this learning is often completely implicit: when asked to explicitly report these regularities observers perform at chance. This implies that visual statistical learning is a powerful process that operates automatically without our intent or conscious control. However, it remains unclear what the benefits of visual statistical learning are for memory processes. In particular, statistical regularities are a form of redundancy, and to the extent that human memory compresses redundant information, learning regularities between objects should enable observers to remember information about more objects. The proposed experiments have three aims: (1) to determine whether visual statistical learning enables observers to compress information and remember more, (2) to determine the "units" of compression, and (3) to determine the "level" at which compression occurs. To investigate these issues, observers will be required to remember simple objects. Over time, some observers will see patterns in the input (e.g., 'A' often occurs with 'B'), while other observers will see random input. To the extent that observers can learn regularities, and compress them to form more efficient memory representations, observers in the patterned group should remember details about more objects. Previous research has not directly explored how visual statistical learning impacts the capacity of working memory, and the proposed studies will provide important insight into the interactions between learning and memory. PUBLIC HEALTH RELEVANCE: An exciting aspect of this proposal is that it promises to inform basic science by studying how statistical learning impacts working memory and cognition, and therefore has potential to increase understanding of mental illnesses with learning or memory related symptoms. For instance, individuals with Autism have certain learning deficits, but it is unknown whether statistical learning, and its interaction with working memory, is impaired in Autism as well. The proposed studies lay the groundwork for future clinical translational research addressing such questions.