Determining Visual Pattern Complexity for Improved Usability in Computerized Cognitive Testing

Abstract

Computerized cognitive tests often entail tasks related to visual stimuli. Most tests are performed by elderly, computer naïve, and sometimes cognitively impaired subjects. Cognitive ergonomics in designing these tests can alleviate the subject's anxiety, and enhance their cognitive evaluation accuracy. One method to achieve this goal is to implement a complexity measure for the cognitive tasks and adjust the complexity level to each subject's capabilities, thus creating an adaptive psychological test. This paper details the design, implementation, and testing of a visual pattern complexity determination algorithm. The patterns used for the study were taken from computerized cognitive assessments. The algorithm was tested using three hundred binary images and compared to the complexity perceived via pair-comparison by human judges. Correlations of 72%, 74%, and 61% between human perception and the algorithm's predictions were obtained for easy, medium, and hard complexity levels, respectively. The algorithm has the potential to become an accurate measure of visual pattern complexity in computerized assessment, and could improve the usability of these tests for psychometric and cognitive evaluations.