Stochastic Models of Visual Decision Making and Visual Search

视觉决策和视觉搜索的随机模型

• 批准号: 10480866
• 负责人: Gordon Dennis Logan
• 金额: $ 38.44万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10480866
• 关键词: Address; Architecture; Area; Attention; Automobile Driving; Behavior; Behavioral; Biophysics; Brain; Collaborations; Complex; Computer Models; Data; Decision Making; Disease; Electroencephalography; Electrophysiology (science); Event-Related Potentials; Exhibits; Eye; Eye Movements; Foundations; Funding; Goals; Human; Individual; Individual Differences; Injury; Intervention; Link; Location; Macaca; Mathematics; Measures; Mind; Modeling; Monkeys; Morphologic artifacts; Movement; Nature; Neurologic; Neurons; Participant; Performance; Pharmacology; Probability; Process; Race; Ramp; Reaction Time; Research; Rest; Saccades; Signal Transduction; Specific qualifier value; Task Performances; Testing; Time; Training; Translational Research; V4 neuron; Vision; Vision Disorders; Visual; Visual Fields; Visual Perception; Visual attention; Visual impairment; Work; base; behavior measurement; behavior prediction; behavioral response; brain behavior; design; disability; experimental study; frontal eye fields; human data; indexing; innovation; insight; mental state; neural circuit; neural model; neurophysiology; predictive modeling; relating to nervous system; response; theories; tool; visual cognition; visual process; visual search

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.

项目摘要 要求支持以促进创新的产品合作，旨在将思想，大脑和 采用性能，神经生理和猴子和电生理测量的行为 人类执行视觉搜索和视觉决策任务。一般目标是得出 使用指定的计算模型，从猴子的尖峰火车到人类行为的连接 精神状态在数学上，将它们链接到脑状态，特别是神经元，并解释神经元如何 计算产生行为。我们的封闭蓄能器模型（GAM）假设 替代响应的阈值证据。模型评估涉及定量测试替代方案 模型体系结构，以预测行为度量，响应可能性和正确分布 以及错误响应时间以及神经量以及它们如何随着设定的大小和目标分配者而变化 从猴子进行视觉搜索的先前收集的数据的可区分性。而我们以前 资助的研究旨在了解GAM中证据积累的结构以及 模型累加因素与FEF中运动相关的神经元的动力学，我们新提出的 研究旨在从计算上理解驱动积累及其其积累的证据的性质 与FEF中视觉响应性神经元的测量动力学的关系。 AIM 1比较 来自视觉响应神经元的侧向脑电图和神经递减的显着证据 对随机蓄能器网络进行视觉搜索作为输入证据以预测的猴子 行为。 AIM 2通过确定是否是否确定是否解决神经蓄能器框架的主要挑战 FEF中的运动神经元动力学实际上是渐变或步骤。 AIM 3评估替代体系结构 驱动积累的证据的抽象视觉注意模型（VAM）共同预测观察者 以及视觉响应性神经元的测量动力学。 AIM 4将VAM扩展到更复杂的视觉任务 涉及过滤和选择。结果将是对视觉过程的广泛理解 选择目标并控制眼动。诸如VAM和GAM之类的计算模型可能处于“正义 右”抽象水平。它们以解释神经活动的方式捕获了计算的基本细节 和单个参与者的行为，无论是猴子还是人类。这些模型可用于理解 正常行为以及疾病，残疾和疾病；最合适的参数可以表征 行为的个体差异，并为大脑测量提供标记。这些模型也可以告知 神经系统疾病在单个神经元和神经循环水平上具有生物物理基础， 提供有关神经元和电路如何计算的洞察力以及它们如何做到的。