Mechanisms of Visual Trajectory Learning & Prediction

视觉轨迹学习的机制

• 批准号: 6738721
• 负责人: David J Freedman
• 金额: $ 4.64万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-12-01 至 2006-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6738721
• 关键词: Macaca mulatta; attention; behavior prediction; behavior test; behavioral /social science research tag; biological signal transduction; eye movements; information seeking behavior; learning; motion perception; neural information processing; neurons; parietal lobe /cortex; postdoctoral investigator; psychomotor reaction time; space perception; startle reaction; visual perception; visual stimulus

DESCRIPTION (provided by applicant): A great deal of evidence supports the notion that visual perception and visually guided behaviors are affected by our predictions about the non-random behavior and interdependencies of objects in the visual world. Previous studies have shown that posterior parietal cortex neurons in area LIP of the monkey can convey predictive signals about the upcoming direction of simple motion (in one dimension) when it was expected by the animal. However, critical issues remain unanswered concerning the mechanisms by which predictive signals develop during learning, the nature of predicted information (i.e. spatial vs. temporal) and how that information is used during visually guided behavior. We propose to address these issues by utilizing a novel behavioral paradigm in which monkeys learn to predict upcoming movements of a target that can follow complex two dimensional trajectories. By recording from neurons in LIP while monkeys learn to predict complex motion paths, we expect to gain substantial insights into the neuronal mechanisms that underlie visual prediction and learning and, further, a more detailed understanding of the nature of spatial information encoded in LIP.

描述（由申请人提供）：大量证据支持这样的观点：视觉感知和视觉引导行为受到我们对视觉世界中物体的非随机行为和相互依赖性的预测的影响。先前的研究表明，猴子 LIP 区域的后顶叶皮层神经元可以在动物预期时传达有关简单运动（在一维）即将到来的方向的预测信号。然而，关于学习过程中预测信号形成的机制、预测信息的性质（即空间与时间）以及在视觉引导行为过程中如何使用该信息的关键问题仍未得到解答。我们建议通过利用一种新颖的行为范式来解决这些问题，在这种范式中，猴子学会预测可以遵循复杂的二维轨迹的目标即将发生的运动。通过在猴子学习预测复杂运动路径时记录 LIP 中的神经元，我们期望能够深入了解视觉预测和学习背后的神经元机制，并进一步更详细地了解 LIP 中编码的空间信息的本质。