It is clear that humans have mental representations of their spatial environments and that these representations are useful, if not essential, in a wide variety of cognitive tasks such as identification of landmarks and objects, guiding actions and navigation and in directing spatial awareness and attention. Determining the properties of mental representation has long been a contentious issue. One method of probing the nature of human representation is by studying the extent to which representation can surpass or go beyond the visual (or sensory) experience from which it derives. From a strictly empiricist standpoint what is not sensed cannot be represented; except as a combination of things that have been experienced. But perceptual experience is always limited by our view of the world and the properties of our visual system. It is therefore not surprising when human representation is found to be highly dependent on the initial viewpoint of the observer and on any shortcomings thereof. However, representation is not a static entity; it evolves with experience. The debate as to whether human representation of objects is view-dependent or view-invariant that has dominated research journals recently may simply be a discussion concerning how much information is available in the retinal image during experimental tests and whether this information is sufficient for the task at hand. Here we review an approach to the study of the development of human spatial representation under realistic problem solving scenarios. This is facilitated by the use of realistic virtual environments, exploratory learning and redundancy in visual detail.