The visual information subtending 3D shape perception and recognition in humans

视觉形状感知对于与环境的有效交互至关重要，也是识别视觉对象的重要基础。在本申请中，我们建议使用一种相对新颖的方法来研究视觉感知，其目的是在没有理论先验或偏见的情况下直接确定视觉信息，该视觉信息涉及正常功能的成年人类观察者深度旋转的视觉形状的识别。通过这里提出的方法获得的新知识应该会带来重大的理论进展，因为它将提供区分竞争理论的三个主要维度： 1-图像特征类型：视觉系统用来表示形状的描述符类别决定了与性能最相关的特征类型。 2-特征稳定性：表示形状的描述符是稳定的还是可变的，根据暴露条件和刺激环境而变化。 3-特征连词：特征连词是否用于编码对象内的空间关系，或者特征之间的空间关系与形状识别无关。我们将首先使用自适应气泡和视觉传播的分类图像技术来确定从不同视点显示的不同类别的合成 3D 形状中识别对象的图像特征。对于每个对象，这些有效位置将跨视点交叉引用，以确定视点不变（即出现在对象的多个视图中）和视点特定（即仅出现在一个视图中）的特征。这些特征将与理想观察者模型使用的特征进行比较，并根据理论上的原理进行表征，以提取可用于预测对于识别新对象集至关重要的图像区域的一般规则。然后将进行测试，以确认这些预测对已使用的对象集的价值（验证测试），并在之前未使用分类图像技术研究过的新对象集中对其进行评估（泛化测试）。在验证和泛化测试期间将记录眼球运动，作为与形状处理相关的特征的附加索引。研究的对象类别在其复杂性和泛化到我们日常生活中遇到的对象的能力方面会有所不同。它们将是： 1- 单几何子（Biederman，1987）； 2- 由四个互连的几何体组成的组件； 3- Hung 等人使用的“表面形状”。 （2012）和山根等人。 (2008) 在他们对猕猴颞下区单神经元形状选择性的研究中，这些区域是相对基本的物体，但可能呈现出丰富的表面形貌； 4- 由 Hung 等人开发的“内侧轴向形状”。 （2012），它们是相当复杂的物体，其中许多让人想起动物或昆虫的形状。阐明视觉形状感知背后的特征在该领域至关重要。当前的提案将通过使用复杂的心理物理学技术（这些技术已证明其能够满足我们的目的）以及使用各种对象类别来对我们的视觉系统在日常对象识别中必须满足的关键约束子集进行采样，从而有助于增进我们对这个问题的了解。

Visual shape perception is crucial for effective interactions with our environment and it is an essential basis for the recognition of visual objects. In the present application, we propose to use a relatively novel approach for the study of visual perception which aims to determine directly, without theoretical a priori or bias, the visual information subtending the recognition of visual shapes that are rotated in depth in normally functioning adult human observers. The new knowledge acquired through the approach proposed here should offer significant theoretical advances since it will inform on three major dimensions that distinguish among competing theories: 1- Type of image feature: The class of descriptors used by the visual system to represent shape determines the type of features that is most relevant for performance. 2- Feature stability: Whether the descriptors by which shapes are represented are stable or variable according to the exposure conditions and stimulus context. 3- Feature conjunctions: Whether feature conjunctions are used to code the spatial relations within an object or alternatively, that the spatial relations among features are irrelevant for shape recognition.We will first use the classification images techniques of Adaptive Bubbles and of Visual Spread to determine the image features that underlie the recognition of objects from different classes of synthetic 3D shapes shown from variable viewpoints. For each object, these effective locations will be cross-referenced across viewpoints to determine features that are either viewpoint invariant (i.e. which occur in more than one view of the object) and viewpoint specific (i.e. which occur in only one view). These features will be compared to those used by an ideal observer model and characterized along theoretically motivated principles to extract general rules that can be applied to predict the image regions that will be critical for the recognition of novel object sets. Tests will then be conducted to confirm the value of these predictions on already used object sets (verification tests) as well as to assess them in new sets of objects not studied before with classification image techniques (generalization tests). Eye movements will be recorded during the verification and generalization tests as an additional index of the features that are relevant for shape processing.The object classes studied will vary in terms of their complexity and their capacity to generalize to objects we encounter in daily life. They will be: 1- single geons (Biederman, 1987); 2- assemblies made of four interconnected geons; 3- the “surface shapes” used by Hung et al. (2012) and Yamane et al. (2008) in their study of single-neuron shape selectivity in the macaque inferotemporal area, which are relatively elementary objects that may nevertheless present a rich surface topography; and 4- “medial axial shapes” developed by Hung et al. (2012), which are rather complex objects, many of them reminiscent of animal or insect shapes.Elucidating the features underlying visual shape perception is of fundamental importance in the field. The current proposal will contribute to advance our knowledge on the issue through its use of sophisticated psychophysical techniques that have proven their capacity for our purposes, and the use of various object classes that will sample a subset of crucial constraints that our visual system must meet for everyday object recognition.