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如何将视觉和转移到目标视觉阵列映射到目标的视觉任务的计算模型，以产生该性能的特定神经过程。阐明此映射提供了融合的约束，以区分竞争模型架构并提供神经电路功能的功能解释。在我们最近开发的视觉搜索过程中，该提案测试，扩展，完善和整合目标选择的两个主要新计算模型的目的。数据将包括在视觉阵列中搜索目标的猴子和人类参与者的表现，在该阵列中，目标位置可以改变以前收集的FEF的神经生理数据的不可预测。这些模型提供了视觉搜索过程中正确和错误扫视行为的详细模式的定量说明，还为额眼场（FEF）中神经元的时间调制提供了解释。与以前的视觉搜索模型不同，我们的在高效且效率低下的搜索过程中的整个正确和错误响应概率和响应时间分布的整个范围，即使目标的位置出乎意料地改变了位置。 AIM 1将开发，完善和扩展扫视目标选择的交互式竞赛模型。我们将测试由多个随机蓄能器（GO单位）组成的竞争模型体系结构，这些架构控制了何时何地进行扫视，其中GO单位之间的相互作用的性质以及在模型变体之间操纵了施加认知控制的停止单元以施加认知控制的可能性。成功的模型预测了猴子和人类中的响应概率和响应时间分布以及先前在猴子中观察到的神经活动。 AIM 2将测试，完善和扩展一个封闭式累加器模型，以将视觉显着性转化为扫视命令。 FEF神经元提供的视觉显着性表示将是对随机GO单元的神经网络的输入，其替代体系结构实施了有关饲料前进，横向抑制和门控抑制作用的竞争假设。 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，其中操纵关键变量以获得测试竞争体系结构的新措施。