Bayesian computations in human 3D visual perception

人类 3D 视觉感知中的贝叶斯计算

基本信息

批准号：
7319279
负责人：
DAVID C KNILL
金额：
$ 35.53万
依托单位：
UNIVERSITY OF ROCHESTER
依托单位国家：
美国
项目类别：
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

项目摘要

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）：拟议研究的目的是了解人类视觉系统如何解决与大多数视觉提示相关的固有的几何歧义。大脑可以通过两种方式解决提示歧义，（1）通过对这些变量的生态限制进行先验知识（例如，数字往往是对称的）和（2）通过合作使用其他感觉提示的信息来消除其价值。前两个主要目标集中在问题的第一部分上。它们的塑造是，观察到使深度信息的大部分统计结构本质上是明确的 - 动作是否僵化，数字是对称的，纹理是否均匀，纹理或不是同质的。在贝叶斯框架内施放问题提供了一个正式的系统，用于建模强大的提示集成，从而使视觉系统能够有效地处理感官提示之间的巨大冲突。我们将执行实验，以测试其他强大的提示集成模型的贝叶斯模型。该模型还提供了一个框架，以表征大脑如何适应其先前统计数据的内部模型，从而使单眼线索提供信息。我们将研究人类观察者如何通过组合多个提示来适应这些内部模型以及如何影响他们如何整合提示以估计表面取向和形状来获得的信息。最终的主要目标测试大脑是否使用非视觉信息（触觉 /动觉），这些信息是由主动运动和对物体探索的探索而得出的，以消除视觉提示所依赖的场景属性。这项研究将集中于三个关于表面取向和形状形状，纹理和运动的单眼视觉线索 - 以及大脑如何将这些线索与立体提示结合在一起。心理物理学是由理想的贝叶斯模型进行视觉提示集成，学习和多模式提示集成的理想贝叶斯模型的计算建模的动机。拟议研究的结果将阐明内置在视觉深度感知基础的神经计算中的统计推断的类型，并定义了这些计算的限制。这最终将指导并限制对愿景神经机制的未来研究。