Role of Expertise in Spatial Thinking

Role of Expertise in Spatial Thinking

Role of Expertise in Spatial Thinking

As with all cognitive competencies, there are significant differences among people as to how, how quickly, and how well they do something. Spatial thinking is no exception. Within domains of knowledge, there are experts and novices. Differences between experts and novices can be accounted for by training and experience. Across domains of knowledge, there are disciplines within which spatial thinking is emphasized and taught and those within which it plays a hidden and relatively minor role. Across groups, there are also significant variations in how people approach spatial thinking. Across age, for example, children and adults do not think spatially in the same way. Those differences can be accounted for by maturation, education, and experience.

All of these issues are addressed in Chapter 4 and Appendix C. If, for the moment, the effects of domain are ignored, the expert-novice distinction can be used to understand some of the major differences in the ways in which people think spatially. A key goal, especially in science, is to learn to extract functional information from spatial structures and to understand how and why something works. In learning to do this, we can order component tasks of spatial thinking in terms of relative difficulty. The first and easiest step is extracting spatial structures. This process of pattern description involves identifying relations between the components of a spatial representation and understanding them in terms of the parts and wholes that give rise to patterns and coherent wholes. The second step, performing spatial transformations, is harder. Translations in space or scale transformations (changes in viewing distance) are easier than rotations or changes of perspective (changes in viewing angle or azimuth). Imagining the motions of different parts in relation to each other running the object can be very difficult.

The third step, drawing functional inferences, is the most difficult and yet the most central to the process of scientific thinking. It requires establishing temporal sequences and cause-and-effect relations. The difficulty of each of these three steps increases with increasing dirnensionality: spatial structures in two-space are easier to understand than those in three-space. In scientific applications, difficulty also increases as a function of data quality and quantity.

Missing data require extrapolation and interpolation. Data error leads to uncertainty and increasing difficulty. Partial and incomplete data require an even more skilled use of extrapolation and interpolation and more complex inference processes. People use representations, whether in the mind orexternal, to comprehend and remember a set of concepts as well as to make inferences and discoveries about those concepts. Understanding the spatial relations and structure of a diagrammed system is relatively straightforward for most learners, because a diagram shows the parts in their spatial relations, using diagrammatic space to map real space. For this part, learning a foreign language needs a leaning tools, many people choose Rosetta Stone English and Rosetta Stone French to learn English and French. Most people can grasp the essential parts and their spatial relations from a diagram, such as a bicycle pump or a heart. What is much harder to understand is the meaning, interpretation, function, and causal chain that the diagram is meant to convey. While a novice can understand the spatial structure of a bicycle pump or heart from a diagram, only those with some expertise can grasp the functional and causal relations among the parts, that is, understand how the pump or the heart works.

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