The interpretation of imagery acquired through technical means is a cognitive skill that is central to nearly every advanced discipline. Reading x-ray, CT, and MRI images is a mainstay in the modern practice of medicine. Interpreting microscope slides is fundamental in histology and pathology. Interpreting a variety of aerial and satellite imagery is standard practice in geology, navigation, and environmental science, as well as in the gathering of national intelligence information. Numerous studies of cognition across the broad array of different types of technical imagery suggest that image interpretation is extremely challenging. Expertise takes many years to acquire, and even highly trained experts continue to make errors. Relatively little is known about what makes the interpretation of technical imagery so challenging. Even less is known about how to improve training methods to make the acquisition of expertise more efficient. This project will advance basic cognitive theory with regard to the fundamental nature of cognition in technical image analysis. The broader impact of this work will be twofold. First, the project will develop empirical techniques for study of technical image analysis that can be applied across different domains of imagery. These techniques will isolate the sources of error at different levels of expertise and lead to the development of instructional techniques to make training more efficient. Second, new training methods that involve the use of 3D computer graphics will be developed to increase the efficiency of training in image analysis and to generate more robust skills in image interpretation. This project will develop instructional techniques that use interactive 3D graphics to encourage a deeper understanding of the nature and variety of technical images in a domain. These techniques will make training in image analysis substantially more efficient and lead to skills that generalize more readily to novel images.
