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

人类 3D 形状感知和识别的视觉信息

• 批准号: RGPIN-2014-03789
• 负责人: Arguin, Martin
• 金额: $ 1.89万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=631151
• 关键词: visual; information; subtending; 3D; shape

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.

视觉形状感知对于与我们环境的有效相互作用至关重要，这是识别视觉对象的重要基础。在介绍应用中，我们建议使用一种相对新颖的方法来研究视觉感知，该方法旨在直接确定而没有理论上的先验或偏见，视觉信息会以正态正常正常的成年人人类观察者的深度旋转视觉形状识别。通过此处提出的方法获得的新知识应提供重大的理论进步，因为它将告知三个主要维度，这些维度将在竞争理论之间区分出来：1-图像特征类型：视觉系统用来表示形状的描述类别的类别确定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，代表形状的描述符是稳定还是可变。 3-特征连接：是否使用特征连接代码对象内的空间关系或替代物之间的空间关系，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的分类图像技术和视觉传播的分类图像技术，并确定图像特征，以确定从可变形状的不同类别中识别对象的识别的图像特征。对于每个对象，这些有效的位置将在观点上交叉引用，以确定视点不变的特征（即，在对象的多个视图中发生）和特定于视点（即仅在一个视图中发生）。这些特征将与理想观察者模型所使用的特征进行比较，并沿理论上动机的原则进行表征，以提取可以应用的一般规则，以预测图像区域，这对于识别新颖对象集至关重要。然后将进行测试以确认这些预测对已经使用的对象集（验证测试）的价值，并在使用分类图像技术（概括测试）之前未研究的新对象集进行评估。眼睛运动将在验证和概括测试期间记录为与形状处理相关的功能的附加索引。研究对象类的复杂性和将其推广到我们日常生活中遇到的对象的能力将有所不同。它们将是：1-单一地球（Biederman，1987）； 2-由四个相互连接的地点制成的组件； 3- Hung等人使用的“表面形状”。 （2012）和Yamane等。 （2008年）在他们研究猕猴的颞叶区域中的单神经元形状选择性时，它们是相对基本物体，但可能仍然具有丰富的表面形貌； Hung等人开发的4-“内侧轴向形状”。 （2012年）是相当复杂的物体，其中许多使人想起了动物或绝缘形状。阐明视觉形状感知的基本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技术来提高我们对问题的知识，这些技术证明了它们的能力出于我们的目的，并且使用各种对象类别，这些类别将对我们的视觉系统每天都必须满足的对象识别的各种对象类别进行采样。