Stochastic Models of Visual Search

视觉搜索的随机模型

• 批准号: 8324575
• 负责人: Gordon Dennis Logan
• 金额: $ 23.4万
• 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 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8324575
• 关键词: Accounting; Architecture; Attention; Behavior; Behavioral; Biological Neural Networks; Brain; Cells; Cognition; Collaborations; Competence; Computer Simulation; Data; Disease; Evaluation; Eye Movements; Feedback; Foundations; Goals; Human; Injury; Intervention; Lateral; Link; Location; Macaca; Maps; Measures; Modeling; Monkeys; Nature; Neurons; Participant; Pattern; Performance; Physiology; Probability; Process; Psychology; Race; Reaction Time; Research; Role; Saccades; Staging; Testing; Time; Training; Translating; Translational Research; Variant; Vision; Visual; Visual attention; base; cognitive control; design; feeding; frontal eye fields; gaze; human data; mathematical model; neural circuit; neurophysiology; programs; relating to nervous system; research study; response; visual performance; visual search

PROJECT SUMMARY The long-term goal of our research is to understand how computational models of performance of visual tasks like locating and shifting gaze to a target a visual array map onto specific neural processes producing that performance. Elucidating this mapping provides converging constraints for discriminating between competing model architectures and provides functional explanations of neural circuit function. The aims of this proposal test, extend, refine, and integrate two major new computational models of target selection during visual search that we have recently developed. Data will consist of performance of monkeys and human participants searching for a target in a visual array in which target location can change unpredictably supplemented by neurophysiological data from FEF that was collected previously. The models provide quantitative accounts of detailed patterns of correct and error saccade behavior during visual search and also provide explanations for the temporal modulation of neurons in frontal eye field (FEF). Unlike previous models of visual search, ours account for the entire range of correct and error response probabilities and response time distributions during efficient and inefficient search, even when the target changes location unexpectedly. Aim 1 will develop, refine, and extend an INTERACTIVE RACE model of saccade target selection. We will test competing model architectures consisting of multiple stochastic accumulators (GO units) that govern when and where a saccade is made, where the nature of the interactions between GO units and the potential inclusion of a STOP unit for exerting cognitive control is manipulated across model variants. Successful models predict response probabilities and response time distributions in monkeys and humans and neural activity observed previously in monkeys. Aim 2 will test, refine, and extend a GATED ACCUMULATOR model of how visual salience is translated into a saccade command. The visual salience representation provided by FEF neurons will be the input to a neural network of stochastic GO units with alternative architectures that implement competing hypotheses about the role of feedforward, lateral and gating inhibition. Aim 3 will integrate these two models. This integration will be guided by new data from human participants performing visual search tasks in which key variables are manipulated to obtain new measures to test competing architectures.

项目总结 我们研究的长期目标是了解视觉任务绩效的计算模型 就像定位并将视线转移到目标上一样，视觉阵列映射到特定的神经过程，产生 性能。阐明这一映射为区分竞争对手提供了收敛的约束 模型架构，并提供神经电路功能的功能说明。这项提议的目的是 测试、扩展、改进和集成视觉搜索过程中目标选择的两个主要新计算模型 这是我们最近开发的。数据将包括猴子和人类参与者的表现 在其中目标位置可能不可预测地改变的视觉阵列中搜索目标 先前收集的FEF的神经生理数据。这些模型提供了定量的解释 视觉搜索期间正确和错误的扫视行为的详细模式，并提供以下解释 前视野神经元的时间调制。与以前的视觉搜索模式不同，我们的 考虑整个范围的正确和错误响应概率以及响应时间分布 搜索效率低下，即使目标意外更改位置也是如此。目标1将会发展， 改进和扩展了一种互动的扫视目标选择的竞赛模型。我们将测试竞争机型 由多个随机累加器(GO单元)组成的架构，用于管理何时何地的扫视 其中GO单元之间的相互作用的性质和潜在的包括停止单元 施加认知控制是在不同的模型变体之间进行的。成功的模型可以预测反应 猴子和人类的概率和反应时间分布以及先前观察到的神经活动 在猴子身上。目标2将测试、改进和扩展一个门控累加器模型，以了解视觉显著程度 翻译成扫视命令。由FEF神经元提供的视觉显著表示将是 输入到具有实现竞争的可选架构的随机围棋单元的神经网络 关于前馈、侧向和门控抑制作用的假说。AIM 3将整合这两种模式。 这种整合将由来自执行视觉搜索任务的人类参与者的新数据指导，在这些任务中 操作关键变量以获得测试竞争体系结构的新措施。