Engineers and technicians : the effect of cognitive style and gender on visual information processing

The purpose of this project was to identify in a subject group of engineers and technicians (N = 62) a preferred mode of representation for facilitating correct recall of information from complex graphics. The modes of representation were black and white (b&w) block, b&w icon, color block, and color icon. The researcher's test instrument included twelve complex graphics (six b&w and six color - three per mode). Each graphics presentation was followed by two multiple-choice questions. Recall performance was better using b&w block mode graphics and color icon mode graphics. A standardized test, the Group Embedded Figures Test (GEFT) was used to identify a cognitive style preference (field dependence). Although engineers and technicians in the sample were strongly field-independent, they were not significantly more field-independent than the normative group in the Witkin, Oltman, Raskin, and Karp study (1971). Tests were also employed to look for any significant difference in cognitive style preference due to gender. None was found. Implications from the project results for the design of visuals and their use in technical training are discussed.

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.