Establishing the Limits of Perceptual Inference for Visual Motion

建立视觉运动感知推理的极限

• 批准号: 10318920
• 负责人: Tyler Manning
• 金额: $ 6.98万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10318920
• 关键词: Address; Adult; Bayesian Analysis; Bayesian Modeling; Behavioral; Biological; Brain; California; Computer Models; Enhancement Technology; Environment; Exposure to; Feedback; Fellowship; Goals; Human; Institution; Investigation; Knowledge; Learning; Literature; Modality; Modeling; Modernization; Molds; Motion; Motion Perception; Outcome; Participant; Perception; Perceptual learning; Performance; Plant Roots; Play; Probability; Psychophysics; Research; Resources; Retina; Role; Sensory; Specialist; Specificity; Speed; Stimulus; System; Technology; Testing; Theoretical Studies; Training; Unconscious State; Universities; Update; Variant; Visual; Visual Motion; Visual Perception; Visual Psychophysics; Visual impairment; Visual system structure; Work; base; computing resources; design; environmental change; experience; experimental study; eye velocity; falls; flexibility; grasp; human subject; ideal observer (Bayesian); insight; next generation; novel; physical property; public health relevance; recruit; response; sensory input; spatial vision; statistics; visual stimulus

Learned statistics about the world play an important role in dictating our sensory perception. When incoming sensory inputs carry limited information, such as in low-contrast conditions like dusk, percepts appear to be heavily dictated by implicit assumptions about the probability of different sensory experiences. More specifically, contemporary research suggests that human visual motion perception is well-described by an ideal observer model that gathers environmental information about motion, but also assumes that objects in the environment are most likely either stationary or moving relatively slowly. Theoretical work implementing this model, referred to as a Bayesian observer with a "slow speed prior", has successfully explained many disparate perceptual studies that found curious biases in perceived motion, and has had far-reaching influence on how we think about human spatial vision. As useful as this slow speed prior hypothesis is, it makes several critical, untested assumptions: namely that the visual system represents a motion prior in an accurate, world- based coordinate system. While this is ideal for a Bayesian observer, it is at odds with evidence from the psychophysical literature on visual motion perception. It is also unclear how flexible this prior is in adults in the face of changing environmental conditions or stimuli. Thus the overarching hypotheses of this proposal are that human representations of motion statistics (1) are updated in the light of strong evidence for either general changes in environmental statistics or changes in stimulus-specific statistics, and (2) are best characterized by coordinate system that is intermediate between retinal and world systems. The proposed research will address hypothesis (1) in Specific Aim 1 by testing whether changes in perceived visual motion following exposure to altered motion statistics are well-explained by updates to slow speed prior that generalizes across stimuli and tasks. This proposal will address hypothesis (2) in Specific Aim 2 by estimating priors under conditions that dissociate retinal and world motion statistics. In each aim, the questions will be studied using a combination of visual psychophysics and computational modeling that formalizes the representation of the priors. Together, these aims will address the substantial gap in the current literature that is marked by a vast number of perceptual learning studies and relatively few studies addressing the ways in which the visual system updates its representation of sensory statistics. These studies will contribute to our knowledge on the fundamental computations involved in the transformation of sensory evidence from the periphery into robust percepts. This fellowship proposal includes a detailed training plan at a world-class research institution (University of California, Berkeley) with several specialists in psychophysical research and access to modern visual display technology and computational resources. The proposed research will take advantage of these resources to design a next-generation model of perceptual inference in the visual system that is well-rooted in physical and biological constraints.

学到的有关世界的统计数据在决定我们的感官知觉方面发挥着重要作用。来电时 感官输入携带的信息有限，例如在黄昏等低对比度条件下，感知似乎是 很大程度上取决于关于不同感官体验的概率的隐含假设。更多的 具体而言，当代研究表明，人类视觉运动感知可以用理想的 观察者模型，收集有关运动的环境信息，但也假设物体在 环境很可能是静止的或移动相对缓慢的。实现这一点的理论工作 模型，被称为具有“慢速先验”的贝叶斯观察者，已经成功地解释了许多 不同的感知研究发现感知运动中存在奇怪的偏差，并产生了深远的影响 关于我们如何看待人类空间视觉。尽管这种慢速先验假设很有用，但它使得几个 关键的、未经测试的假设：即视觉系统代表了一个准确的、世界范围内的运动。 为基础的坐标系。虽然这对于贝叶斯观察者来说是理想的，但它与来自贝叶斯观察者的证据不一致 关于视觉运动感知的心理物理学文献。目前还不清楚这种先验对于成年人来说有多灵活 面对不断变化的环境条件或刺激。因此，该提案的总体假设是 运动统计的人类表示 (1) 根据任何一般性的有力证据进行更新 环境统计数据的变化或特定刺激统计数据的变化，以及 (2) 的最佳特征是 介于视网膜坐标系和世界坐标系之间的坐标系。拟议的研究将解决 具体目标 1 中的假设 (1) 通过测试暴露于 改变的运动统计数据可以通过更新慢速先验来很好地解释，该更新概括了刺激和 任务。该提案将通过在以下条件下估计先验来解决特定目标 2 中的假设 (2)： 分离视网膜和世界运动统计。在每个目标中，将结合使用以下方法来研究问题： 视觉心理物理学和计算模型将先验的表示形式化。一起， 这些目标将解决当前文献中存在的巨大差距，即大量的 知觉学习研究和相对较少的研究涉及视觉系统更新的方式 它是感官统计的表示。这些研究将有助于我们了解基础知识 涉及将外围感官证据转化为稳健感知的计算。这 奖学金提案包括在世界一流研究机构（英国大学）的详细培训计划 加利福尼亚州伯克利）拥有几位心理物理学研究专家和现代视觉显示技术 技术和计算资源。拟议的研究将利用这些资源 设计视觉系统中的下一代感知推理模型，该模型植根于物理和 生物限制。