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Applying a Handwriting Measurement Model for Capturing Cognitive Load Implications Through Complex Figure Drawing

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Abstract

The aim of the study was to examine the application of a computerized handwriting model for characterizing complex figure-drawing performance. We posit that spatial, temporal, and pressure measures that reflect figure-drawing behavior will differ significantly under two mental workload conditions, and that both drawing and handwriting process measures will predict the quality of what is drawn and/or written. Thirty participants copied the Rey–Osterrieth Complex Figure Test (ROCFT). They then reproduced it from memory and finally copied a paragraph on a digitizer that is part of the Computerized Penmanship Evaluation Tool (ComPET) system. Results indicated that certain computerized measures of the ROCFT copying significantly correlated with those of the paragraph-copying behavior (r = .38–.75). Significant differences were found between the spatial and temporal computerized measures of performance in the ROCFT copying and drawing-from-memory tasks. Stepwise regressions indicated that mean pressure predicted 12 % of the variance of the ROCFT and paragraph-copying quality scores and 6 % of the ROCFT drawing-from-memory score. Furthermore, 52 % of the variance of the ROCFT drawing-from-memory score was predicted by the mean velocity. The benefits and significance of obtaining computerized measures of the drawing process for better insight about human performance characteristics are discussed, and applications are suggested.

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    Authors and Affiliations

    1. Laboratory of Complex Human Activity and Participation (CHAP), Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, 31905, Israel

      Sara Rosenblum

    2. Department of Human Services, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel

      Gil Luria

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    1. Sara Rosenblum
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    2. Gil Luria
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    Correspondence to Sara Rosenblum.

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    Sara Rosenblum and Gil Luria have contributed equally to this article.

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    Rosenblum, S., Luria, G. Applying a Handwriting Measurement Model for Capturing Cognitive Load Implications Through Complex Figure Drawing. Cogn Comput 8, 69–77 (2016). https://doi.org/10.1007/s12559-015-9343-y

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