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可以利用环境中的记忆来记住更多信息。我们之前的经历如何影响我们对当前经历的记忆？如何利用这些影响作为工具来增强学习障碍者的认知过程（例如，或者帮助那些有记忆缺陷的人？为了解决这些问题，该建议利用了一种强大的内隐学习形式，称为视觉统计学习。先前的研究表明，观察者对视觉环境中的干扰极其敏感（例如，（通常，“A”后面跟着“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不确定性时，观察者的表现是偶然的。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意识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的好处。特别是，统计学上的重复是一种冗余形式，在人类记忆压缩冗余信息的程度上，学习对象之间的重复应该使观察者能够记住更多对象的信息。这些实验有三个目的：（1）确定视觉统计学习是否能使观察者压缩信息并记住更多，（2）确定压缩的“单位”，（3）确定压缩发生的“水平”。为了研究这些问题，观察者需要记住简单的物体。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。如果观察者能够学习记忆，并将其压缩以形成更有效的记忆表征，那么模式组中的观察者应该记住更多物体的细节。以往的研究还没有直接探讨视觉统计学习如何影响工作记忆的容量，拟议的研究将提供重要的洞察学习和记忆之间的相互作用。 公共卫生相关性：这一提议的一个令人兴奋的方面是，它有望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损。拟议的研究为未来解决这些问题的临床转化研究奠定了基础。

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.