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.

项目摘要 我们研究的长期目标是了解视觉任务性能的计算模型 像定位并将目光转移到目标视觉阵列图上，以产生特定的神经过程 表现。阐明此映射提供融合的约束，以区分竞争 模型体系结构并提供神经回路功能的功能解释。该提议的目的 在视觉搜索期间测试，扩展，完善和整合目标选择的两个主要新计算模型 我们最近发展了。数据将包括猴子和人类参与者的表现 在视觉阵列中搜索目标位置可以改变不可预测的目标位置 先前收集的FEF的神经生理数据。这些模型提供了定量帐户 在视觉搜索过程中正确和错误扫视行为的详细模式，还提供了解释 神经元在额眼场（FEF）的时间调节。与以前的视觉搜索模型不同，我们 说明正确和错误响应概率和响应时间分布的整个范围 即使目标意外改变位置，效率和效率低下的搜索也是如此。 AIM 1将发展， 完善并扩展了扫视目标选择的交互式竞赛模型。我们将测试竞争模型 由多个随机蓄能器（GO单元）组成的体系结构，这些堆积物（GO单位）在扫视何时何地组成 是在GO单元之间的相互作用的性质和潜在的停止单元的性质 在模型变体之间操纵认知控制。成功的模型预测响应 先前观察到的猴子和人类的概率和响应时间分布以及神经活动 在猴子。 AIM 2将测试，完善和扩展一个封闭的累加器模型，以了解视觉显着性的方式 翻译成扫视命令。 FEF神经元提供的视觉显着性表示将是 输入随机GO单元的神经网络，该单元具有实施竞争的替代体系结构 关于喂食，侧向和门控抑制作用的作用的假设。 AIM 3将整合这两个模型。 该集成将以执行视觉搜索任务的人类参与者的新数据为指导 操纵关键变量以获得测试竞争体系结构的新措施。