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

视觉形状感知对于我们与环境的有效互动至关重要，它是识别视觉物体的重要基础。在目前的应用中，我们建议使用一种相对新颖的方法来研究视觉感知，其目的是直接确定，没有理论上的先验或偏见，在正常功能的成年人类观察者中，对深度旋转的视觉形状的识别所对应的视觉信息。通过这里提出的方法获得的新知识应该提供重要的理论进步，因为它将告知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的类别决定了与性能最相关的特征类型。2-特征稳定性：根据暴露条件和刺激环境，表示形状的描述符是稳定的还是可变的。3-特征连词：是否使用特征连词来编码物体内部的空间关系，或者是否使用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形状中识别物体的图像特征。对于每个对象，这些有效位置将在多个视点之间进行交叉引用，以确定视点不变（即出现在对象的多个视图中）和视点特定（即只出现在一个视图中）的特征。这些特征将与理想观察者模型所使用的特征进行比较，并按照理论激励原则进行特征化，以提取可用于预测图像区域的一般规则，这些规则对于识别新对象集至关重要。然后将进行测试，以确认这些预测对已经使用的对象集的价值（验证测试），以及在以前未使用分类图像技术研究过的新对象集中评估它们（泛化测试）。在验证和概化测试期间，将记录眼球运动，作为与形状处理相关的特征的附加指标。所研究的对象类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。他们将是：1-单身（Biederman, 1987）；2-由四个相互连接的母线组成的组件；3- Hung et al.（2012）和Yamane et al.（2008）在研究猕猴颞下区单神经元形状选择性时使用的“表面形状”，它们是相对基本的物体，但可能呈现丰富的表面形貌；4- Hung等人（2012）开发的“中轴形状”，这是相当复杂的物体，其中许多让人想起动物或昆虫的形状。阐明视觉形状感知的基本特征在该领域具有重要意义。目前的提案将有助于推进我们对这个问题的认识，通过使用复杂的心理物理技术，这些技术已经证明了它们的能力，并使用各种物体类别，这些类别将采样我们的视觉系统必须满足日常物体识别的关键约束子集。

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.