The human brain uses small differences between the images reaching our two eyes to perceive the three-dimensional shape of the world around us. In order to detect these differences, known as binocular disparities, the brain must find points in one eye's image that match to points in the other eye's image. However, for any single point in an image, the brain is often forced to choose between multiple matches. The problem of finding the correct match from amongst these alternatives is known as the correspondence problem. This problem can be simplified by making assumptions about the typical shape of objects in the world, and by finding matches between different kinds of basic tokens. For example, the number of alternative solutions to the correspondence problem will be far greater if the brain uses single points of light as a basic token for matching, compared to a case where a more complex token, such as a shape or texture, is used. Furthermore, these complex tokens can be based on different kinds of information. The research proposed here examines how matching tokens based on different forms of information can be used by the brain to solve the correspondence problem. Specifically, we shall examine how the brain may solve the correspondence problem using tokens derived from mechanisms sensitive to changes in light and dark (i.e. changes in luminance), and mechanisms sensitive to changes in texture. We shall develop computer simulations of the processes used by the brain to solve the correspondence problem and measure disparity. These simulations will show how the use of different basic information for matching (i.e. changes in luminance and changes in texture) can change the nature of the correspondence problem. We shall discover whether the combined use of texture- and luminance-based matching tokens can help to reduce noise in disparity measurement and whether the use of texture-based matching can reduce the number of available solutions to the correspondence problem. Following this, we shall examine whether the brain actually makes use of the combined information available from texture and luminance. By presenting human participants with images containing disparities defined by both texture and luminance, we shall establish whether the human brain actually uses these different types of information to reduce noise, or improve its ability to solve the correspondence problem. In addition to examining whether using luminance and texture information to measure disparity helps the brain to reduce noise and simplify the correspondence problem, we shall also examine whether sensitivity to these different types of image information can help the brain to detect discontinuities in depth. Depth discontinuities arise when depth changes sharply across a small area, such as when an observer's view of one object is partially obscured by another object in front of it. The processing of texture-based disparities may help in the detection of depth discontinuities since different objects often differ in texture. We shall establish whether information of this kind may actually be useful in the detection of depth discontinuities, and whether human observers actually use this information.