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.

学习到的关于世界的统计数据在决定我们的感官知觉方面扮演着重要的角色。当来电时 感觉输入携带的信息有限，例如在黄昏这样的低对比度条件下，感觉似乎是 这在很大程度上取决于关于不同感官体验的可能性的隐含假设。更多 具体地说，当代研究表明，人类的视觉运动知觉可以用一种理想来很好地描述 观察者模型，收集有关运动的环境信息，但也假设 环境很可能不是静止的，就是相对缓慢地移动。贯彻落实这一原则的理论工作 贝叶斯观测者模型成功地解释了许多 不同的感知研究发现感知到的运动存在奇怪的偏见，并产生了深远的影响 关于我们如何看待人类的空间视觉。尽管这一低速先验假设很有用，但它得出了几个结论 关键的、未经检验的假设：即视觉系统代表了一个准确的、世界上先前的运动- 基于坐标系。虽然这对于贝叶斯观察者来说是理想的，但它与来自 关于视觉运动知觉的心理物理学文献。目前也不清楚这种先天因素在成人中的灵活性有多大。 面对不断变化的环境条件或刺激。因此，这项提议的首要假设是 运动统计的人类表示(1)根据两种一般情况下的有力证据进行更新 环境统计数据的变化或特定于刺激的统计数据的变化，以及(2)最好的特征是 介于视网膜和世界坐标系之间的坐标系。拟议的研究将解决 假设(1)通过测试暴露于特定目标1后知觉视觉运动的变化 改变的运动统计数据可以很好地解释更新到低速之前，它概括了刺激和 任务。这一建议将通过在下列条件下估计先验来解决具体目标2中的假设(2) 分离视网膜和世界运动统计数据。在每个目标中，这些问题将通过以下组合来研究 视觉心理物理学和计算模型，将先验的表示形式形式化。一起， 这些目标将解决当前文学中的巨大差距，这一差距的标志是大量的 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 它代表的是感官统计。这些研究将有助于我们对 将感官证据从外围转化为强健感知所涉及的计算。这 研究金提案包括在世界级研究机构(哥伦比亚大学)的详细培训计划 加州，伯克利)，有几位心理物理研究专家和现代视觉显示 技术和计算资源。拟议的研究将利用这些资源来 在视觉系统中设计下一代知觉推理模型，该模型植根于物理和 生物学上的限制。