Bayesian computations in human 3D visual perception

人类 3D 视觉感知中的贝叶斯计算

• 批准号: 7663053
• 负责人: DAVID C KNILL
• 金额: $ 30.8万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7663053
• 关键词: Behavior; Biological Models; Brain; Computer Simulation; Conflict (Psychology); Coupled; Cues; Data; Depth Perception; Future; Goals; Human; Image; Judgment; Knowledge; Lead; Learning; Measures; Modality; Modeling; Motion; Movement; Nature; Perception; Problem Solving; Process; Property; Psychophysics; Psychophysiology; Research; Retinal; Scheme; Sensory; Shapes; Signal Transduction; Source; Statistical Models; Structure; Surface; Testing; Texture; Uncertainty; Vision; Visual; Visual Perception; Visual system structure; grasp; haptics; improved; monocular; neuromechanism; object shape; optic flow; programs; relating to nervous system; research study; statistics; stereoscopic; theories; visual feedback; visual information

DESCRIPTION (provided by applicant): The goal of the proposed research is to understand how the human visual system resolves the inherent geometric ambiguities associated with most visual cues to depth. The brain can resolve cue ambiguity in two ways, (1) by applying prior knowledge of ecological constraints on those variables (e.g. that figures tend to be symmetric) and (2) by cooperatively using the information from other sensory cues to disambiguate their values. The first two principal aims focus on the first part of the problem. They are shaped by the observation that much of the statistical structure that makes monocular cues to depth informative is categorical in nature - motions are rigid or not, figures are symmetric or not, textures are homogeneous or not, etc.. We will study how the visual system combines information from multiple cues to disambiguate which of the several possible prior constraints to use when interpreting a cue. Casting the problem within a Bayesian framework provides a formal system for modeling robust cue integration, which allows the visual system to effectively deal with large conflicts between sensory cues. We will perform experiments to test the Bayesian model against other models of robust cue integration. The model also provides a framework for characterizing how the brain adapts its internal models of the prior statistics that make monocular cues informative. We will study how human observers use the information obtained by combining multiple cues to adapt these internal models and how this impacts how they integrate cues to estimate surface orientation and shape. The final principal aim tests whether and how the brain uses non-visual information (haptic / kinesthetic) derived from active movement and exploration of objects to disambiguate scene properties on which visual cues depend. The research will focus on three monocular visual cues about surface orientation and shape- figure shape, texture and motion - and how the brain combines these cues with stereoscopic cues. The psychophysics is motivated by and will be coupled with computational modeling of ideal Bayesian models for visual cue integration, learning and multi-modal cue integration. The results of the proposed research will elucidate the types of statistical inferences that are built into the neural computations underlying visual depth perception and define the limits of these computations. This will ultimately direct and constrain future studies of the neural mechanisms underlying vision.

描述（由申请人提供）：拟议研究的目标是了解人类视觉系统如何解决与大多数视觉线索深度相关的固有几何模糊性。大脑可以通过两种方式解决线索歧义，（1）通过将生态约束的先验知识应用于这些变量（例如，图形往往是对称的），以及（2）通过合作使用来自其他感官线索的信息来消除其值的歧义。前两个主要目标集中在问题的第一部分。它们是由观察形成的，即使单眼线索深度信息的统计结构在本质上是分类的-运动是否刚性，图形是否对称，纹理是否均匀等。我们将研究视觉系统如何结合来自多个线索的信息，以消除在解释线索时使用的几个可能的先验约束中的哪一个。铸造的贝叶斯框架内的问题提供了一个正式的系统建模强大的线索整合，这使得视觉系统能够有效地处理感官线索之间的大冲突。我们将进行实验，以测试贝叶斯模型对其他模型的强大的线索整合。该模型还提供了一个框架，用于表征大脑如何调整其先验统计数据的内部模型，使单目线索提供信息。我们将研究人类观察者如何使用通过组合多个线索来适应这些内部模型所获得的信息，以及这如何影响他们如何整合线索来估计表面方向和形状。最终的主要目的是测试大脑是否以及如何使用来自主动运动和探索对象的非视觉信息（触觉/动觉）来消除视觉线索所依赖的场景属性的歧义。这项研究将集中在三个关于表面方向和形状的单目视觉线索--图形形状、纹理和运动--以及大脑如何将这些线索与立体线索结合起来。心理物理学的动机是，并将与理想的贝叶斯模型的视觉线索整合，学习和多模态线索整合的计算建模。拟议研究的结果将阐明内置于视觉深度感知的神经计算中的统计推断类型，并定义这些计算的限制。这将最终指导和限制未来的视觉神经机制的研究。