Behavioral and neural mechanisms of visual short-term memory

项目总结 这项工作的目标是对视觉短视的机制有一个基本的、定量的了解。 健康中的术语记忆(VSTM)。VSTM的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。更好的 VSTM丢失的特征可能为治疗任务指明方向，以帮助恢复部分丢失。这 研究方案在本质上依赖于将心理物理学与神经科学相结合。 领先的VSTM模型断言，VSTM是一种无噪音的存储，具有固定、有限的 可容纳约4件物品。多余的项目，如果有的话，将不会被记住。我们提出了另一种理论 指出了VSTM在低水平视觉神经机制方面的局限性。感官信息 随之而来的是不确定性，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，它是 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。 将相同的概念应用于VSTM，我们假设：1)由于神经的原因，不确定性随着集合大小的增加而增加 2)大脑对不确定的输入进行概率推理。我们称之为不确定性模型。 目标1：检验不确定性模型和固定容量模型中哪一个更好地解释延迟 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 测量受试者的不确定性作为集合大小的函数。我们将检验VSTM是有限的这一假设 通过固定的能力，但通过对连续分布在项目之间的神经资源的限制。 目标2：检验不确定性模型和固定容量模型中哪个能更好地解释变化 检测性能。变化检测是研究VSTM的主要范式。我们将测试 一种假设，即观察者通过计算变化的概率来最佳地检测不确定情况下的变化 给出噪声观察(概率推理)，类似于低水平视觉任务。 目标3：测试人类观察者以最佳方式整合可能性和先验知识的假设 更改检测。最佳观察者在逐项和逐项试验中使用不确定性知识 在下游计算中的基础上。为了测试人类在变化检测中是否这样做，我们将改变 在固定的集合大小下，分别通过操纵对比和总体任务统计来确定可能性或先验。 目的4：建立视觉变化检测的神经基础模型。从实验结果中得知 在目标1-3中，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.