Shape constancy in action and perception

准确、灵巧的手部动作来抓取和操纵物体的能力是人类的一个定义性行为特征。这些动作通常只需要很少的脑力劳动，这就给人一种“常识”的印象，即它们背后的视觉系统和运动系统之间的相互作用是非常直接的：我们看到了目标物体，我们抓住了它。但事情并不那么简单。例如，考虑一下伸手去拿智能手机的任务。如果要求我们指定使手指到达需要位置的几何形状，我们将不知所措。至少，我们需要描述手机的结构，以及从手机表面到我们的肢体及其各个部分的距离和角度。然而，当我们在无数日常不同的环境中伸手去拿手机时，我们的视觉系统会对视觉阵列进行采样，并执行必要的计算，以便在几分之一秒内启动触达。伸手去够一个物体是一个复杂的过程，它涉及到视觉、触觉、注意力、记忆、运动和执行控制等多个系统。该研究项目的目的是进一步探索2D和3D物体结构的几何描述如何解释我们如何在一系列视点和观看距离上感知和作用于物体。在短期内，我建议：（1）研究如何解释和指导抓取点的选择是由二维和三维物体形状的几何描述，如中轴线及其相关的半径/距离函数;（2）开发和测试一个模型的抓取点的选择是基于这些几何描述;以及（3）使用MRI引导的经颅磁刺激来观察外侧枕叶和前顶叶内皮层在计算用于强迫选择物体分类任务的中轴和选择用于抓握的稳定接触点中起什么作用。本研究项目采用了一种视觉系统模型，该模型使用中轴变换作为基础来推导结构表示和属性，以帮助识别和识别对象以及选择抓握点。由于其计算性质及其与计算机视觉和图像处理的共振，基于中轴的抓取点选择模型是机器人和人工智能应用的一个有前途的途径，此外还为使用EEG的基础研究提供了新的方向，神经成像和单单位记录。该研究项目的长期目标是设计和实现能够灵活，人性化的抓取和操纵的机器人，这些机器人可以部署在从辅助护理到危险和/或人类无法接近的环境中，特别是需要手动操纵形状和大小不可预测的新物体的环境中。

The ability to make accurate, dexterous hand movements for grasping and manipulating objects is a defining behavioural feature of humans. These actions typically involve little mental effort, giving rise to the `common-sense' impression that the interaction between the visual and motor systems that underlie them is quite straightforward: i.e., we see the target object, and we grasp it. But things are not that simple. Consider, for example, the task of reaching for our smart phone. We would be at a loss if asked to specify the geometry that gets the fingers where they need to be. At minimum, we would need a description of the phone's structure and the distances and angles from the phone's surface to our limb and its various segments. And yet, when we reach for our phone in a myriad of everyday different contexts, our visuomotor system samples the visual array and performs the necessary computations to initiate the reach in a fraction of a second. Reaching for an object is a complex process that operates at the intersection of a host of systems involving vision, haptics, attention, memory, motor, and executive control. The purpose of this research program is to further explore how well geometric descriptions of the structure of 2D and 3D objects can explain how we perceive and act on objects across a range of viewpoints and viewing distances. In the short term, I propose: (1) to examine how well grasp-point selection is explained and guided by geometric descriptions of 2D and 3D object shape, such as the medial axis and its associated radius/distance function; (2) to develop and test a model of grasp point selection that is based on these geometric descriptions; and (3) to use MRI-guided transcranial magnetic stimulation to see what role the lateral-occipital and anterior intraparietal cortices play in computing the medial axis for forced-choice object classification tasks and the selection of stable contact points for grasping. This research program adopts a model of the visual system that uses the medial axis transform as a basis to derive structural representations and properties that assist with identifying and recognizing objects and with the selection of grasp points. Due to its computational nature and its resonance with computer vision and image processing, a model of grasp point selection based on the medial axis stands as a promising avenue for application in robotics and artificial intelligence, in addition to providing new directions for basic research using EEG, neuroimaging, and single-unit recordings. The long-term goals of this research program are geared towards the design and implementation of robots capable of flexible, human-like grasps and manipulation that can be deployed in environments ranging from assisted care to those that are hazardous and/or inaccessible to humans and, in particular, where the manual manipulation of novel objects of unpredictable shape and size is required.