Quantitative Model Design for Optimizing the Difficulty of Cognitive Games 


Vol. 15,  No. 1, pp. 37-45, Jan.  2026
10.3745/TKIPS.2026.15.1.37


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  Abstract

This study proposes a novel Difficulty Index (ID) model to quantitatively assess cognitive load using cognition-based games and validates it through comparison with existing alternative models. The experiment required participants to perform a task in which they entered digits from 0 to 9 in reverse order according to the presentation speed. A total of 60 difficulty conditions were designed by combining the number of digits and display time. The proposed model was defined to reflect the limitations of working memory capacity, mathematically expressing the assumption that difficulty increases exponentially when the number of digits exceeds four. For comparison, a simple linear model and a logarithmic-scale model adapted from Fitts’ Law were employed. Regression analysis revealed that the proposed model exhibited the highest explanatory power and demonstrated superior predictive performance compared to the other two models. These findings suggest that the proposed model effectively captures the nonlinear increase in task difficulty and can serve as a reliable indicator for predicting actual user performance. Furthermore, by integrating theoretical foundations of working memory with experimental validation, this study provides a practical tool for designing educational and training environments that balance challenge and learning efficiency, thereby offering both academic and practical contributions.

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[IEEE Style]

K. S. Yeon and L. Y. Hoon, "Quantitative Model Design for Optimizing the Difficulty of Cognitive Games," The Transactions of the Korea Information Processing Society, vol. 15, no. 1, pp. 37-45, 2026. DOI: 10.3745/TKIPS.2026.15.1.37.

[ACM Style]

Kim Se Yeon and Lim Young Hoon. 2026. Quantitative Model Design for Optimizing the Difficulty of Cognitive Games. The Transactions of the Korea Information Processing Society, 15, 1, (2026), 37-45. DOI: 10.3745/TKIPS.2026.15.1.37.

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