Behavioral and neural mechanisms of visual short-term memory

描述(由申请人提供)：这项工作的目标是对健康中视觉短期记忆(VSTM)的机制有一个基本的、定量的了解。VSTM缺陷存在于许多疾病中，包括视觉忽视、顶叶和额叶损伤、注意力缺陷/多动障碍和精神分裂症。对VSTM丢失的更好的描述可能会为治疗任务指明方向，以帮助恢复部分损失。这一研究方案依赖于将心理物理学与神经科学相结合的基本方式。领先的VSTM型号声称，VSTM是一种无噪音的存储，其固定的有限容量约为4个项目。多余的项目，如果有的话，将不会被记住。我们提出了一种替代理论，该理论从低水平视觉的神经机制方面揭示了VSTM的局限性。感觉信息伴随着不确定性，部分原因是神经的可变性。在像线索组合这样的简单知觉任务中，众所周知，人类在这样的不确定性下进行概率推理来优化性能。将同样的概念应用于VSTM，我们假设：1)由于神经约束，不确定性随着集合大小的增加而增加；2)大脑对不确定输入进行概率推理。我们称之为不确定性模型。目的1：检验不确定性模型和固定容量模型哪个更好地解释延迟估计性能。受试者估计一件记忆物品的身份。我们将使用两个不同的任务来衡量受试者的不确定性作为集合大小的函数。我们将检验这一假设，即VSTM不是受固定容量的限制，而是受连续分布在项目之间的神经资源的限制。目的2：检验不确定性模型和固定容量模型哪个更好地解释变化检测性能。变化检测是研究VSTM的主要范式。我们将检验这一假设，即观察者通过计算给定噪声观察(概率推理)的改变的概率来最佳地检测不确定条件下的改变，类似于低水平视觉任务。目的3：检验人类观察者在变化检测中以最佳方式整合可能性和先验信息的假设。最优观察者在下游计算中逐个项目和逐个试验地使用不确定性知识。为了测试人类在变化检测中是否这样做，我们将在固定的集合大小下，通过分别操作对比度和总体任务统计数据来改变可能性或先验。目的4：建立视觉变化检测的神经基础模型。根据AIMS 1-3中的实验结果，我们将构建一个用于变化检测的行为约束神经网络。我们将使用概率种群编码的理论框架。由此产生的网络将完全基于不确定性模型，但呈现出容量限制的外观。它将作为生理测试的基础。 与公共健康相关：视觉短期记忆缺陷存在于多种形式的脑损伤和疾病中，包括视觉忽视、顶叶和额叶病变、注意力缺陷/多动障碍和阿尔茨海默病。在这里，我们建议通过实验和理论更好地描述视觉短期记忆背后的行为和神经机制，最终目标是改进这些疾病的诊断和治疗。

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.