TY - GEN
T1 - Visual pattern complexity determination for enhanced usability in cognitive testing
AU - Babshet, Kanaka
AU - Honegger, Catherine
AU - Gritzman, Ashley
AU - Aharonson, Vered
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - Computerized cognitive tests often entail tasks related to visual stimuli. An efficient complexity measure for these tasks can enhance their cognitive evaluation accuracy, specifically for elderly and cognitively impaired subjects. This paper details the design, implementation, and testing of a visual pattern complexity determination algorithm. The patterns used for the study are sixteen-bit binary patterns taken from computerized cognitive assessments. Three complexity levels were defined based on the visual perception of human subjects: easy, medium, and hard. The algorithm was tested on three hundred patterns and the results were compared to the parallel complexities perceived by human judges. Correlations of 72%, 74%, and 61% between human perception and the algorithm’s predictions were obtained for the easy, medium, and hard patterns, respectively. The algorithm has 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 - Computerized cognitive tests often entail tasks related to visual stimuli. An efficient complexity measure for these tasks can enhance their cognitive evaluation accuracy, specifically for elderly and cognitively impaired subjects. This paper details the design, implementation, and testing of a visual pattern complexity determination algorithm. The patterns used for the study are sixteen-bit binary patterns taken from computerized cognitive assessments. Three complexity levels were defined based on the visual perception of human subjects: easy, medium, and hard. The algorithm was tested on three hundred patterns and the results were compared to the parallel complexities perceived by human judges. Correlations of 72%, 74%, and 61% between human perception and the algorithm’s predictions were obtained for the easy, medium, and hard patterns, respectively. The algorithm has 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 - Binary images
KW - Cognitive assessments usability
KW - Human perceived complexity
KW - Visual pattern complexity
UR - https://www.scopus.com/pages/publications/85021792316
U2 - 10.1007/978-3-319-60642-2_18
DO - 10.1007/978-3-319-60642-2_18
M3 - Conference contribution
AN - SCOPUS:85021792316
SN - 9783319606415
T3 - Advances in Intelligent Systems and Computing
SP - 195
EP - 203
BT - Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2017 International Conference on Neuroergonomics and Cognitive Engineering, 2017
A2 - Baldwin, Carryl
PB - Springer Verlag
T2 - AHFE 2017 International Conference on Neuroergonomics and Cognitive Engineering, 2017
Y2 - 17 June 2017 through 21 June 2017
ER -