Behavioral and neural mechanisms of visual short-term memory

项目摘要 这项工作的目标是获得一个基本的，定量的了解机制的视觉短- 术语记忆（VSTM）在健康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/多动症和精神分裂症。更好的 VSTM损失的表征可以为治疗任务指明方向，以帮助恢复一些损失。这 研究建议主要依赖于将心理物理学与神经科学相结合。 VSTM模型的主要类别断言VSTM是具有固定、有限 容量约为4件。额外的项目，如果有的话，将不会被记住。我们提出了另一种理论， 投射VSTM在低层次视觉的神经机制方面的局限性。感觉信息 伴随着不确定性，部分原因是神经变异性。在简单的知觉任务中，如线索组合，它是 众所周知，人类执行概率推理以优化这种不确定性下的性能。 将相同的概念应用于VSTM，我们假设：1）由于神经网络，不确定性随着集合大小而增加 约束; 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：检验不确定性模型或固定容量模型是否能更好地解释延迟 估计性能受试者估计所记忆项目的身份。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一个函数的大小。我们将检验VSTM有限的假设， 通过固定的容量，但是通过对在项目之间连续分布的神经资源的约束。 目标2：检验不确定性模型或固定容量模型是否能更好地解释变化 检测性能变化检测是研究VSTM的一个主要范式。我们将测试 假设观察者通过计算变化的概率来最佳地检测不确定性下的变化 考虑到噪声观测（概率推断），类似于低水平视觉任务。 目的3：检验人类观察者最佳地整合似然性和先验的假设， 变化检测一个最佳的观察员使用的知识不确定性的一个项目到项目和试验到试验 下游计算的基础。为了测试人类是否在变化检测中做到这一点，我们将改变 可能性或先验，在固定的集合大小，分别通过操纵对比度和整体任务统计。 目的4：建立视觉变化检测的神经基础模型。根据实验结果 在目标1-3中，我们将构建一个行为约束神经网络用于变化检测。我们将使用 概率总体编码的理论框架。由此产生的网络将完全基于 不确定性模型，但表现出容量限制的外观。它将作为生理测试的基础。

PROJECT SUMMARY 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.