Continuous strength, population-based representations in visual working memory

视觉工作记忆可以让人们在一段短时间内记住信息，以便他们可以使用这些信息来执行任务(例如，当你在杂乱无章的橱柜中寻找时，记住哪个杯子是你的)。这个存储系统非常有限。例如，即使在很短的时间范围内，我们也很难保持关于几个物体的准确信息。目前的建议使用新的计算建模和实验技术来更好地理解视觉工作记忆中存储了关于项目的哪些信息。重要的是，一个人能够记住的信息量(即工作记忆中的信息量)已被证明与许多其他认知能力(包括智力)有关，工作记忆障碍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至关重要。提高我们对视觉工作记忆系统的理解也可能改善我们理解视觉技能的执行情况，例如那些涉及驾驶和其他复杂任务的技能，这些任务需要同时记住和监控多个位置和物体(例如，跟踪其他车道上的汽车的位置以决定合并是否安全)。这项工作还可以通过提供关于哪些视觉元素对用户来说最容易(或最难)用户记忆或在其间导航的更深层次的知识来通知显示器和用户界面的设计。除了主要的研究，这项建议还包括一个重要的推广部分，包括从代表性不足的群体中招募和培训第一代大学生，以及开发软件，使科学家能够更好地测量记忆表现，并通过允许招募更广泛、更具代表性的个体样本的方法来实现这一点(即，通过基于互联网的实验)。该研究项目试图了解我们的工作记忆系统记住关于一个物体的哪些信息。现有的视觉工作记忆理论通常假设一个特征的记忆只由一个值来表示(例如，对于颜色记忆，“我认为沙发是‘红色的’”)。相比之下，这项建议使用了连续复制任务的计算模型，即参与者必须复制一组物品的确切颜色或形状，以便测试连续强度人口代码的签名。本项目提出的记忆的替代概念假设记忆不是由单个值表示，而是由特征的每个可能值的激活值来表示(例如，沙发颜色的记忆不仅是‘红色’，而且是每种颜色的值的范围)。这项提案中的研究测试了人们是否能够获得特征的概率分布，而不仅仅是哪个单一特征最能反映他们的记忆。它还测试项目之间的可变性(在精确度和置信度方面)是否可以从这些表示法中预测。这里提出的新理论框架--植根于信号检测理论--不仅允许理解记忆错误等客观指标，还可以预测记忆强度以及与记忆强度相关的主观自信心。该框架还与工作记忆的神经群体编码模型有密切的相似之处。因此，这项工作在工作记忆容量、记忆精度和信心之间建立了新的联系，并在工作记忆的神经模型和认知模型之间架起了一座桥梁。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Visual working memory allows people to hold information in mind for a brief period of time so that they may use that information to perform a task (e.g., holding in mind which mug is yours as you look for it in a cluttered cabinet). This memory system is very limited. For example, we struggle to retain accurate information about just a few objects even over short timescales. The current proposal uses novel computational modeling and experimental techniques to better understand what information is stored about items in visual working memory. Importantly, the amount of information a person is able to hold in mind (i.e., 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. New knowledge regarding how this information is stored and used is therefore critical for understanding the nature of important differences between individuals. Improving our understanding of the visual working memory system may also improve how we understand the execution of visual skills such as those involved in driving and other complex tasks which require simultaneous remembering and monitoring of multiple locations and objects (e.g., tracking the positions of cars in other lanes to decide whether it is safe to merge). This work may also inform the design of displays and user-interfaces by providing deeper knowledge of which visual elements are easiest (or hardest) for users to remember or navigate between. Beyond the primary research, this proposal also includes a significant outreach component, featuring both the recruitment and training of first-generation college students from underrepresented groups, as well as the development of software to allow scientists to better measure memory performance and to do so via methods that allow for recruitment of a wider, more representative sample of individuals (i.e., via internet-based experiments).This research project seeks to understand what information our working memory system holds in mind about an object. Existing theories of visual working memory commonly assume that a memory for a feature is represented by just a single value (e.g., for color memory, “I think the couch was ‘red’”). By contrast, this proposal uses computational models of continuous reproduction tasks, whereby participants must reproduce the exact color or shape of a set of items in order to test for signatures of continuous strength population-codes. The alternative conception of memory advanced by this project assumes that memory is not represented by a single value, but by an activation value for each possible value of a feature (e.g., memory for the color of the couch is not just ‘red’ but a range of values for each color). The research in this proposal tests whether people have access to a probability distribution over features, rather than simply what single feature best reflects their memory. It also tests whether the variability between items (in precision and confidence) is predictable from such representations. The new theoretical framework advanced here – which is rooted in signal detection theory – not only allows for an understanding of objective measures like memory error, but also makes predictions about memory strength and the subjective feeling of confidence associated with memory strength. The framework also shares close parallels with neural population coding models of working memory. Thus, this work makes novel connections between working memory capacity, memory precision and confidence, and provides a bridge between neural and cognitive models of 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.