Behavioral and neural mechanisms of visual short-term memory

视觉短期记忆的行为和神经机制

• 批准号: 8111839
• 负责人: Wei Ji Ma
• 金额: $ 29.47万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8111839
• 关键词: Accounting; Affect; Alzheimer' s Disease; Appearance; Attention; Attention deficit hyperactivity disorder; Behavior; Behavioral Mechanisms; Biological Neural Networks; Brain; Brain Diseases; Brain Injuries; Buffers; Code; Cues; Data; Detection; Diagnosis; Disease; Exhibits; Goals; Health; Human; Knowledge; Lesion; Light; Location; Measures; Memory Loss; Modeling; Nature; Neurons; Neurosciences; Noise; Parietal; Parietal Lobe; Performance; Physiological; Population; Probability; Psychophysics; Receiver Operating Characteristics; Reporting; Research Proposals; Resources; Sampling; Schizophrenia; Science; Sensory; Short-Term Memory; Signal Transduction; Stimulus; Temporal Lobe; Testing; Uncertainty; Vision; Visual; Visual attention; Work; base; frontal lobe; improved; insight; mathematical model; neglect; neural model; neuromechanism; public health relevance; relating to nervous system; research study; statistics; theories; trafficking; visual information; visual search; visual stimulus

DESCRIPTION (provided by applicant): The goal of this work is to gain a fundamental, quantitative understanding of the mechanisms of visual short- term memory (VSTM) in health. Deficits in VSTM are found in numerous disorders, including visual neglect, parietal and frontal lobe damage, attention deficit/hyperactivity disorder, and schizophrenia. A better characterization of VSTM loss may point the direction of therapy tasks to help restore some of the loss. This research proposal relies in an essential way on integrating psychophysics with neuroscience. The leading class of VSTM models asserts that VSTM is a noiseless storage with a fixed, limited capacity of about 4 items. Extra items, if any, will not be remembered. We propose an alternative theory that casts the limitations of VSTM in terms of the neural mechanisms of low-level vision. Sensory information comes with uncertainty, in part due to neural variability. In simple perceptual tasks like cue combination, it is well known that humans perform probabilistic inference to optimize performance under such uncertainty. Applying the same concepts to VSTM, we postulate that: 1) uncertainty increases with set size due to a neural constraint; 2) the brain performs probabilistic inference on uncertain inputs. We call this the uncertainty model. Aim 1: To test whether the uncertainty model or fixed-capacity models better explain delayed estimation performance. Subjects estimate the identity of a remembered item. We will use two distinct tasks to measure subjects' uncertainty as a function of set size. We will test the hypothesis that VSTM is limited not by a fixed capacity, but by a constraint on neural resources which are distributed continuously among items. Aim 2: To test whether the uncertainty model or fixed-capacity models better explain change detection performance. Change detection is a leading paradigm for studying VSTM. We will test the hypothesis that observers optimally detect changes under uncertainty by computing the probability of a change given the noisy observations (probabilistic inference), in analogy to low-level visual tasks. Aim 3: To test the hypothesis that human observers optimally integrate likelihoods and priors in change detection. An optimal observer uses knowledge of uncertainty on an item-to-item and trial-to-trial basis in downstream computation. To test whether humans do this in change detection, we will vary either the likelihood or a prior, at fixed set size, by manipulating contrast and overall task statistics, respectively. Aim 4: To model the neural basis of visual change detection. Informed by the experimental findings in Aims 1-3, we will construct a behaviorally constrained neural network for change detection. We will use the theoretical framework of probabilistic population coding. The resulting network will be entirely based on the uncertainty model but exhibit the appearance of a capacity limit. It will serve as a basis for physiological tests. PUBLIC HEALTH RELEVANCE: Deficits in visual short-term memory are found in many forms of brain damage and disease, including visual neglect, parietal and frontal lesions, attention deficit/hyperactivity disorder, and Alzheimer's disease. Here, we propose to better characterize, through experiment and theory, the behavioral and neural mechanisms underlying visual short-term memory, with the eventual goal of improving the diagnosis and treatment of these disorders.

描述（由申请人提供）：这项工作的目的是获得对健康中视觉短期记忆（VSTM）机制的基本，定量的理解。 VSTM中的缺陷存在许多疾病，包括视觉忽视，顶叶和额叶损害，注意力缺陷/多动障碍和精神分裂症。更好地表征VSTM损失可能会指出治疗任务的方向，以帮助恢复一些损失。这项研究提案以一种基本的方式依赖于将心理物理学与神经科学融为一体。 VSTM模型的领先类别断言VSTM是一种无嘈杂的存储空间，容量固定约4个项目。额外的物品（如果有的话）将不会被记住。我们提出了一种替代理论，该理论在低级视力的神经机制方面施加了VSTM的局限性。感官信息带有不确定性，部分原因是神经变异性。在简单的感知任务（例如提示组合）中，众所周知，人类在这种不确定性下进行概率推断以优化性能。将相同的概念应用于VSTM，我们假设：1）由于神经约束而导致的不确定性随着设定的大小而增加； 2）大脑对不确定输入的概率推断。我们将其称为不确定性模型。 目标1：测试不确定性模型或固定容量模型是否更好地解释了延迟的估计性能。受试者估计了记忆项目的身份。我们将使用两个不同的任务来测量受试者的不确定性作为设定大小的函数。我们将检验以下假设：VSTM不受固定能力的限制，而是受到对在项目之间不断分布的神经资源的限制。 目标2：测试不确定性模型或固定容量模型是否更好地解释变化检测性能。变更检测是研究VSTM的主要范例。我们将测试以下假设：观察者通过计算嘈杂的观察值（概率推论）来最佳检测不确定性下的变化，这与低级视觉任务相比。 目的3：测试人类观察者最佳地整合可能性和先验的变化检测的假设。最佳观察者在下游计算中使用项目对项目和试验基础的不确定性知识。为了测试人类是否在变化检测中执行此操作，我们将分别通过操纵对比度和整体任务统计数据来改变可能性或以固定设置大小的可能性。 目标4：建模视觉变化检测的神经基础。在AIMS 1-3中的实验结果中得知，我们将构建一个行为约束的神经网络以进行变更检测。我们将使用概率种群编码的理论框架。所得网络将完全基于不确定性模型，但显示出容量极限的外观。它将作为生理测试的基础。 公共卫生相关性：视觉短期记忆的缺陷在多种形式的脑损伤和疾病中发现，包括视觉忽视，顶叶和额叶病变，注意力不足/多动障碍以及阿尔茨海默氏病。在这里，我们建议通过实验和理论更好地表征视觉短期记忆的行为和神经机制，最终是改善这些疾病的诊断和治疗的目标。