Visual processing of contours and textures

这个申请是为我继续研究轮廓和纹理的视觉处理提供资金。检测轮廓形状（如梨的轮廓）和纹理结构（如地毯或树皮上的图案）的视觉能力，对于识别图案、物体和纹理表面的形状至关重要。我的实验使用行为或“心理物理学”方法对正常人进行测试。在一个典型的实验中，受试者在显示器上观看大量专门设计的计算机生成的测试模式，并被要求对每个模式做出强制选择判断；例如，两种形状中哪一种是前面展示的，两种图案中哪一种排列更规则，两种纹理中哪一种包含圆形结构。我的一个实验方法也利用了一种被称为“视觉后效”的现象。长时间观看，称为“适应”，一个特定的轮廓形状或纹理，导致随后呈现的轮廓/纹理出现扭曲。通过测量扭曲的程度，我们可以了解到大脑是如何处理轮廓和纹理信息的。例如，纹理图案的一个基本特征是规律性，或“重复性”，例如地毯上的重复图案。如果一个人采用高度规则的纹理模式，比如在网格上完美排列的一组点，并适应它，那么稍微不那么规则的模式看起来甚至比其他方式更不规则。这种效应被称为“规律后效”，而它发生的事实表明，大脑中有专门负责观察规律的神经元。通过测量各种纹理图案的规律性后效应，并构建神经元如何编码规律性的计算机模型，人们可以弄清楚大脑是如何检测和编码纹理图案中的规律性的。因此，结合我在形状编码和纹理方面的其他实验，我的研究将有助于更好地理解大脑如何看待轮廓的形状和纹理的结构。

This application is for funds to continue my studies into the visual processing of contours and textures. The visual ability to detect the shapes of contours, such as the outline of a pear, and the structure of textures, such as the pattern on a carpet or the bark of a tree, is essential for recognizing patterns, objects and the shapes of textured surfaces. My experiments use behavioural or ‘psychophysical’ methods with normal human test subjects. In a typical experiment, subjects view large numbers of specially-designed computer-generated test patterns presented on a display monitor and are required to make for each a forced-choice judgement; for example which of two shapes is the one previously shown, which of two patterns is the more regularly arranged, which of two textures contains a circular structure. One of my experimental methods also exploits a phenomenon termed the ‘visual after-effect’. Prolonged viewing, termed ‘adaptation’, of a particular contour shape or texture causes subsequently presented contours/textures to appear distorted. By measuring the amount of distortion, much can be learnt about how the brain processes contour and texture information. For example, a fundamental feature of texture patterns is regularity, or ‘repeatedness’, for example as in the repeating pattern of a carpet. If one takes a highly regular texture pattern, such as an array of dots arranged perfectly on a grid, and adapts to it, a slightly less regular pattern appears even less regular than otherwise. This effect is termed the ‘regularity after-effect’, and the fact that it happens at all suggests that there are neurons in the brain that are specialized for seeing regularity. By measuring the regularity aftereffect for various types of texture pattern and constructing computer models of how the neurons might encode regularity, one can work out how the brain detects and encodes regularity in a texture pattern. Together with my other experiments on shape coding and texture, my research will therefore lead to a better understanding of how the brain sees the shapes of contours and the structure of textures.