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arXiv · 2412.14188

CogSimulator: A Model for Simulating User Cognition & Behavior with Minimal Data for Tailored Cognitive Enhancement

Weizhen Bian·Yubo Zhou·Yuanhang Luo·Ming Mo·Siyan Liu·Yikai Gong·Renjie Wan·Ziyuan Luo·Aobo Wang
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Abstract

The interplay between cognition and gaming, notably through educational games enhancing cognitive skills, has garnered significant attention in recent years. This research introduces the CogSimulator, a novel algorithm for simulating user cognition in small-group settings with minimal data, as the educational game Wordle exemplifies. The CogSimulator employs Wasserstein-1 distance and coordinates search optimization for hyperparameter tuning, enabling precise few-shot predictions in new game scenarios. Comparative experiments with the Wordle dataset illustrate that our model surpasses most conventional machine learning models in mean Wasserstein-1 distance, mean squared error, and mean accuracy, showcasing its efficacy in cognitive enhancement through tailored game design.

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Weizhen Bian, Yubo Zhou, Yuanhang Luo, Ming Mo, Siyan Liu, Yikai Gong, Renjie Wan, Ziyuan Luo, Aobo Wang. 2024-12-10. CogSimulator: A Model for Simulating User Cognition & Behavior with Minimal Data for Tailored Cognitive Enhancement. https://arxiv.org/abs/2412.14188

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