Shape constancy in action and perception

做出准确、灵巧的手部动作来抓取和操纵物体的能力是人类的一种决定性的行为特征。这些动作通常不需要多少脑力劳动，给人一种“常识性”印象，即视觉和运动系统之间的互动是非常直接的：也就是说，我们看到目标物体，然后抓住它。但事情并没有那么简单。举个例子，想想我们拿起智能手机的任务。如果我们被要求具体说明手指需要的几何形状，我们会不知所措。至少，我们需要描述手机的结构，以及手机表面到我们肢体及其各个部分的距离和角度。然而，当我们在无数不同的日常环境中拿起手机时，我们的视觉运动系统会对视觉阵列进行采样，并执行必要的计算，在几分之一秒内启动触摸。伸手拿东西是一个复杂的过程，涉及视觉、触觉、注意力、记忆、运动和执行控制等一系列系统。这项研究计划的目的是进一步探索二维和三维物体结构的几何描述如何很好地解释我们如何在一系列视点和观看距离上感知和行动物体。在短期内，我建议：(1)研究二维和三维物体形状的几何描述（如中轴及其相关的半径/距离函数）如何很好地解释和指导抓点选择；(2)开发并测试基于这些几何描述的抓点选择模型；(3)使用mri引导的经颅磁刺激来观察枕侧皮质和顶叶前皮质在计算强迫选择对象分类任务的内侧轴和选择抓取的稳定接触点方面所起的作用。本研究方案采用视觉系统模型，以中轴变换为基础，推导出有助于识别和识别物体以及选择抓点的结构表征和属性。由于其计算性质及其与计算机视觉和图像处理的共鸣，基于内侧轴的抓点选择模型在机器人和人工智能中具有很好的应用前景，此外还为使用脑电图、神经成像和单单元记录的基础研究提供了新的方向。这项研究计划的长期目标是设计和实现能够灵活的、类似人类的抓取和操作的机器人，这些机器人可以部署在从辅助护理到危险和/或人类无法进入的环境中，特别是需要手动操作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.