TY - GEN
T1 - Determining visual pattern complexity for improved usability in computerized cognitive testing
AU - Honegger, Catherine
AU - Babshet, Kanaka
AU - Gritzman, Ashley
AU - Aharonson, Vered
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/9/19
Y1 - 2017/9/19
N2 - Computerized1 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.
AB - Computerized1 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.
KW - Applied human factors and ergonomics
KW - Binary images
KW - Cognitive assessment
KW - Neuroergonomics
KW - Perceived complexity
KW - Visual pattern complexity
UR - https://www.scopus.com/pages/publications/85033467168
U2 - 10.1145/3121283.3121308
DO - 10.1145/3121283.3121308
M3 - Conference contribution
AN - SCOPUS:85033467168
T3 - ACM International Conference Proceeding Series
SP - 195
EP - 198
BT - 35th Annual Conference of the European Association of Cognitive Ergonomics
PB - Association for Computing Machinery
T2 - 35th Annual Conference of the European Association of Cognitive Ergonomics, ECCE 2017
Y2 - 20 September 2017 through 22 September 2017
ER -