The role of short-term memory uncertainty in visual decision-making

项目总结/摘要 这个项目的目标是获得一个基本的定量了解的机制 视觉短期记忆（VSTM）在健康。VSTM是人类认知的一个核心方面， 让人们能够感知环境的变化。在VSTM的缺陷被发现在许多 视觉和神经障碍，包括视觉忽视、顶叶和额叶损伤， 注意力缺陷/多动障碍、脑外伤和精神分裂症。更好的 VSTM的表征可能会为更好地诊断和治疗此类缺陷开辟道路。 在其第一阶段，该项目推翻了对自然的既定信念， VSTM的局限性。该项目的继续重点是VSTM的丰富性， 特别是假设VSTM包含一个与不确定性相关的表示， 短期记忆为了验证这一假设，研究人员使用了一组新的行为测试。 任务，其中一些涉及受试者直接报告他们对记忆的信心， 另一些则是这样设计的，即决策性能得益于利用VSTM的知识 不确定性与其他使用线条画或字母等刺激物的作品相比， 在这个项目中的实验使用简单的，单一的特征刺激，允许两个紧密的实验 控制和精确的数学建模。 该项目将数学建模与实验有机地结合在一起。 马云实验室是大规模测试行为数学模型的领导者， 使用最先进的统计技术进行比较，并使用结果来告知 进一步的实验设计。该项目中的模型旨在精确表征 大脑通过这些过程做出基于VSTM的决定。一大类模型 将被测试的是基于观察者最优或接近最优的联合收割机组合的概念 VSTM信息与先验信息和奖励信息达成决策。超越行为 模型，该项目将使用神经网络建模来解决神经电路如何可能 实现项目的行为任务中涉及的计算。具体而言是 研究人员询问大脑如何能够将VSTM不确定性的知识整合起来， 明确地接受过关于不确定性的正确值的训练。 该项目更广泛的影响包括将两个传统上完全不同的 研究领域，即VSTM研究和决策研究。除了潜力 临床应用，该项目将导致更好地了解大脑如何 在巨大的不确定性面前做出正确的决定。

PROJECT SUMMARY/ABSTRACT The goal of this project is to gain a fundamental quantitative understanding of the mechanisms of visual short-term memory (VSTM) in health. VSTM is a central aspect of human cognition, allowing people to detect changes in the environment. Deficits in VSTM are found in many visual and neurological disorders, including visual neglect, parietal and frontal lobe damage, attention deficit/hyperactivity disorder, traumatic brain injury, and schizophrenia. A better characterization of VSTM may lead the way to better diagnosis and treatment of such deficits. In its first period, this project has overturned established beliefs about the nature of the limitations of VSTM. The continuation of the project focuses on the richness of VSTM and in particular the hypothesis that VSTM contains a representation of uncertainty associated with a short-term memory. To test this hypothesis, the investigators use a set of novel behavioral tasks, some of which involve subjects directly reporting their confidence about a memory, while others are designed such that decision performance benefits from utilizing knowledge of VSTM uncertainty. In contrast to other work that uses stimuli such as line drawings or letters, all experiments in this project use simple, single-feature stimuli, allowing for both tight experimental control and precise mathematical modeling. The project integrates mathematical modeling with the experiments in an essential way. The Ma laboratory is a leader in the large-scale testing of mathematical models of behavior, comparing them using state-of-the-art statistical techniques, and using the results to inform further experimental design. The models in this project are aimed at precisely characterizing the processes by which the brain arrives at a VSTM-based decision. One large category of models that will be tested is based on the notion that observers optimally or near-optimally combine VSTM information with prior and reward information to reach a decision. Beyond behavioral models, the project will use neural network modeling to address how neural circuits might implement the computations involved in the behavioral tasks of the project. Specifically, the investigators ask how the brain can incorporate knowledge of VSTM uncertainty without being explicitly trained on the correct value of uncertainty. The broader impact of the project consists of connecting two traditionally disparate research areas, namely the study of VSTM and the study of decision-making. Besides potential clinical applications, the project will lead to a better basic understanding of how the brain manages to make good decisions in the face of great uncertainty.