Stochastic Models of Visual Search

视觉搜索的随机模型

• 批准号: 8536300
• 负责人: Gordon Dennis Logan
• 金额: $ 22.23万
• 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 2014-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8536300
• 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

DESCRIPTION (provided by applicant): 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 feed forward, 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.

描述(由申请人提供)：我们研究的长期目标是了解视觉任务绩效的计算模型如何映射到产生这种绩效的特定神经过程，如定位和将目光转移到目标视觉阵列。阐明这种映射为区分相互竞争的模型体系结构提供了收敛约束，并提供了神经电路功能的功能解释。这项提案的目的是测试、扩展、改进和整合我们最近开发的两个主要的视觉搜索期间目标选择的计算模型。数据将包括猴子和人类参与者在视觉阵列中搜索目标的表现，其中目标位置可能会发生不可预测的变化，并补充之前收集的来自FEF的神经生理学数据。这些模型提供了视觉搜索过程中正确和错误眼跳行为的详细模式的定量描述，也解释了前视视野(FEF)神经元的时间调制。与以前的视觉搜索模型不同，我们的模型考虑了在高效和低效搜索期间的整个范围的正确和错误响应概率以及响应时间分布，即使目标意外改变位置也是如此。目标1将开发、改进和扩展一个扫视目标选择的交互式竞赛模型。我们将测试由多个随机累加器(GO单元)组成的竞争模型架构，这些随机累加器(GO单元)控制何时何地进行扫视，其中GO单元之间交互的性质以及用于实施认知控制的潜在停止单元是在模型变体中操作的。成功的模型预测了猴子和人类的反应概率和反应时间分布，以及以前在猴子身上观察到的神经活动。目标2将测试、改进和扩展一个门控累加器模型，该模型将如何将视觉突显转换为扫视命令。由FEF神经元提供的视觉显著表征将作为随机围棋单位的神经网络的输入，该网络具有不同的体系结构，实现关于前馈、侧向和门控抑制作用的相互竞争的假设。AIM 3将整合这两种模式。这种整合将由执行视觉搜索任务的人类参与者提供的新数据指导，在这些任务中，关键变量被操纵以获得测试竞争架构的新措施。