Stochastic Models of Visual Decision Making and Visual Search

项目摘要 要求提供支持，以推动创新的、富有成效的合作，旨在将思想、大脑和 使用猴子的表现、神经生理学和电生理学测量的行为， 人类执行视觉搜索和视觉决策任务。一般目标是导出 从猴子的尖峰列车到人类行为的联系，使用计算模型， 精神状态数学，将它们与特定神经元的大脑状态联系起来，并解释神经元是如何 计算产生行为。我们的门控累加器模型（GAM）假设随机累加 替代反应的阈值证据。模型评估涉及定量测试替代方案 模型架构的预测行为措施，响应概率和分布的正确 和错误反应时间，以及神经措施，以及这些如何与设置大小和目标分心物的变化 先前从猴子进行视觉搜索收集的数据的可辨别性。虽然我们以前 资助的研究旨在了解GAM中证据积累的架构以及 我们新提出的FEF中运动相关神经元的动态模型， 研究的目的是通过计算来理解驱动这种积累的证据的性质及其 与FEF中视觉反应神经元的测量动力学的关系。目标1比较了 侧化EEG信号和视觉反应神经元的神经放电的显著性证据， 猴子进行视觉搜索作为输入证据的网络随机预测 行为Aim 2通过确定神经累加器框架是否 FEF中的运动神经元动力学实际上是斜坡或台阶。Aim 3评估了 抽象的视觉注意力模型（VAM）的证据驱动积累，以共同预测观察到的行为 以及视觉反应神经元的测量动态。Aim 4将VAM扩展到更复杂的视觉任务 包括过滤和选择。其结果将是对视觉过程的更广泛和更深入的理解 来选择目标并控制眼球运动。像VAM和GAM这样的计算模型可能处于“公正”的位置， 抽象的“层次”。它们以解释神经活动的方式捕捉计算的基本细节 和行为，无论是猴子还是人类。这些模型可以用来理解 正常行为以及疾病，残疾和疾病;最佳拟合参数可以表征 行为的个体差异，并为大脑测量提供标记。这些模型还可以告知 在个体神经元和神经回路水平上具有生物物理基础的神经病症， 深入了解神经元和电路的计算内容以及它们如何进行计算。

PROJECT SUMMARY Support is requested to advance an innovative, productive collaboration aimed at linking mind, brain, and behavior using performance, neurophysiological, and electrophysiological measures from monkeys and humans performing visual search and visual decision making tasks. The general goal is to derive the connections from spike trains in monkeys to behavior in humans using computational models that specify mental states mathematically, link them to brain states in particular neurons, and explain how the neural computations produces behavior. Our Gated Accumulator Model (GAM) assumes a stochastic accumulation of evidence to threshold for alternative responses. Model assessment involves quantitatively testing alternative model architectures on predictions of behavioral measures, response probabilities and distributions of correct and error response times, as well as neural measures and how these change with set size and target-distractor discriminability in previously collected data from monkeys performing visual search. While our previously funded research aimed to understand the architecture of evidence accumulation in GAM and the relationship of model accumulators to the observed dynamics of movement-related neurons in FEF, our newly proposed research aims to understand computationally the nature of the evidence that drives that accumulation and its relationship to the measured dynamics of visually-responsive neurons in FEF. Aim 1 compares the quality of salience evidence in lateralized EEG signals and neural discharges from visually-responsive neurons in monkeys performing visual search as input evidence to a network of stochastic accumulators to predict behavior. Aim 2 addresses a major challenge to the neural accumulator framework by determining whether movement neuron dynamics in FEF actually ramp or step. Aim 3 evaluates alternative architectures for an abstract Visual Attention Model (VAM) of the evidence driving accumulation to jointly predict observed behavior and the measured dynamics of visually-responsive neurons. Aim 4 extends VAM to more complex visual tasks involving filtering and selection. The result will be a broader and deeper understanding of the visual processes that select targets and control eye movements. Computational models like VAM and GAM may be at the “just right” level of abstraction. They capture essential details of the computation in ways that explain neural activity and behavior in single participants, whether monkey or human. These models can be used to understand normal behavior as well as illness, disability, and disease; the best-fitting parameters can characterize individual differences in behavior and provide markers for brain measures. These models can also inform neurological conditions that have a biophysical basis at the level of individual neurons and neural circuits, offering insight into what neurons and circuits compute and how they do it.