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International Conference on Applied Human Factors and Ergonomics

AHFE 2017: Advances in Neuroergonomics and Cognitive Engineering pp 195–203Cite as

Visual Pattern Complexity Determination for Enhanced Usability in Cognitive Testing

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 586))

Abstract

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.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa

    Kanaka Babshet, Catherine Honegger & Vered Aharonson

  2. IBM Research, 47 Juta Street, Braamfontein, Johannesburg, South Africa

    Ashley Gritzman

Authors
  1. Kanaka Babshet
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  2. Catherine Honegger
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  3. Ashley Gritzman
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  4. Vered Aharonson
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Corresponding author

Correspondence to Kanaka Babshet .

Editor information

Editors and Affiliations

  1. Department of Psychology, Human Factors and Applied Cognition, George Mason University, Fairfax, Virginia, USA

    Carryl Baldwin

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© 2018 Springer International Publishing AG

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Babshet, K., Honegger, C., Gritzman, A., Aharonson, V. (2018). Visual Pattern Complexity Determination for Enhanced Usability in Cognitive Testing. In: Baldwin, C. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2017. Advances in Intelligent Systems and Computing, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-60642-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-60642-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60641-5

  • Online ISBN: 978-3-319-60642-2

  • eBook Packages: EngineeringEngineering (R0)

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