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

项目摘要/摘要 这个项目的目标是对这些机制有一个基本的定量的了解。 视觉短时记忆(VSTM)对健康的影响。VSTM是人类认知的中心方面， 使人们能够检测到环境的变化。VSTM的缺陷存在于许多 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、创伤性脑损伤和精神分裂症。更好的 VSTM的特征化可能有助于更好地诊断和治疗此类缺陷。 在它的第一阶段，这个项目颠覆了关于 VSTM的局限性。该项目的继续侧重于VSTM的丰富性和 特别是假设VSTM包含与 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 任务，其中一些任务涉及受试者直接报告他们对记忆的置信度，而 其他设计使决策绩效受益于利用VSTM的知识 不确定性。与其他使用线条画或字母等刺激的工作不同，所有的 本项目中的实验使用简单的单一特征刺激，允许进行紧凑的实验 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。 MA实验室在大规模测试行为的数学模型方面处于领先地位， 使用最先进的统计技术对它们进行比较，并使用结果来 进一步的实验设计。这个项目中的模型旨在准确地描述 大脑做出基于VSTM的决策的过程。一大类车型 这将被检验是基于这样一个概念，即观察者以最佳或接近最佳的组合 VSTM信息与事前和奖励信息一起做出决策。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 实施项目行为任务中涉及的计算。具体地说， 研究人员问，大脑如何在不存在的情况下整合VSTM不确定性的知识 明确训练不确定的正确值。 该项目的更广泛影响包括将两个传统上截然不同的 研究领域，即VSTM研究和决策研究。除了潜力 临床应用，该项目将使人们更好地基本了解大脑是如何 在巨大的不确定性面前设法做出正确的决定。

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.