Stochastic Models of Visual Search

视觉搜索的随机模型

• 批准号: 8161053
• 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/8161053
• 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. PUBLIC HEALTH RELEVANCE: The models tested and refined through this research plan will provide a firm foundation from which to understand disorders of visual attention, orientation and mobility that are consequences of impaired visual search. Elucidation of the mapping between effective mathematical models of behavior and specific brain processes is necessary for translational research seeking to understand how vision and cognition are impacted by injury, disease, or pharmacological interventions.

描述（由申请人提供）：我们研究的长期目标是了解视觉任务（如定位和转移视线到目标视觉阵列）的计算模型如何映射到产生该性能的特定神经过程。阐明这一映射提供了收敛的约束条件之间的竞争模型架构进行区分，并提供了神经电路功能的功能解释。这个建议的目的是测试，扩展，完善，并整合两个主要的新的计算模型，我们最近开发的视觉搜索过程中的目标选择。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，其中目标位置可以不可预测地变化，并由先前收集的FEF神经生理数据补充。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说明，也提供了对额叶眼场（FEF）神经元的时间调制的解释。与以前的模型的视觉搜索，我们的帐户的整个范围内的正确和错误的响应概率和响应时间分布在高效和低效的搜索，即使当目标意外地改变位置。目标1将发展，完善和扩展一个扫视目标选择的交互式竞赛模型。我们将测试竞争模型架构，包括多个随机的随机数（GO单元），控制何时何地进行扫视，其中GO单元之间的相互作用的性质和潜在的STOP单元之间的相互作用的性质，用于施加认知控制，在模型变量中进行操纵。成功的模型预测猴子和人类的反应概率和反应时间分布以及之前在猴子中观察到的神经活动。目标2将测试，完善和扩展门控累积模型的视觉显着性是如何转化为扫视命令。由FEF神经元提供的视觉显著性表示将是随机GO单元的神经网络的输入，该随机GO单元具有替代架构，该替代架构实现关于前馈、横向和门控抑制的作用的竞争假设。Aim 3将整合这两种模式。这种集成将由执行视觉搜索任务的人类参与者的新数据指导，其中操纵关键变量以获得测试竞争体系结构的新措施。 公共卫生关系：通过本研究计划测试和完善的模型将为理解视觉搜索受损的后果视觉注意力，方向和移动性障碍提供坚实的基础。阐明行为的有效数学模型和特定大脑过程之间的映射对于寻求理解视觉和认知如何受到损伤，疾病或药物干预影响的转化研究是必要的。