Bayesian computations in human 3D visual perception

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深度线索相关的固有几何模糊性。大脑可以通过两种方式解决线索模糊性，（1）通过对这些变量应用生态约束的先验知识（例如，数字往往是对称的）和（2）通过合作使用来自其他感官线索的信息来消除它们的值的歧义。前两个主要目标集中于问题的第一部分。它们是通过观察而形成的，即使单眼线索提供深度信息的许多统计结构本质上是分类的——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组合来自多个线索的信息，以消除在解释线索时使用几种可能的先验约束中的哪一个的歧义。在贝叶斯框架内解决问题提供了一个用于建模鲁棒线索集成的正式系统，这使得视觉系统能够有效地处理感官线索之间的巨大冲突。我们将进行实验来测试贝叶斯模型与其他稳健提示集成模型的对比。该模型还提供了一个框架，用于描述大脑如何适应先前统计数据的内部模型，从而使单眼线索提供丰富的信息。我们将研究人类观察者如何使用通过组合多个线索获得的信息来适应这些内部模型，以及这如何影响他们整合线索来估计表面方向和形状的方式。最终的主要目标是测试大脑是否以及如何使用源自主动运动和物体探索的非视觉信息（触觉/动觉）来消除视觉线索所依赖的场景属性的歧义。该研究将重点关注关于表面方向和形状的三种单眼视觉线索——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。心理物理学的动力来自于理想贝叶斯模型的计算模型，用于视觉线索整合、学习和多模态线索整合。拟议研究的结果将阐明视觉深度感知基础神经计算中内置的统计推断类型，并定义这些计算的限制。这最终将指导和限制未来对视觉神经机制的研究。

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.