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

TL;DR

CogSimulator is a new algorithm that accurately simulates user cognition with minimal data, aiding personalized cognitive enhancement in educational gaming contexts.

Contribution

It introduces a novel model using Wasserstein-1 distance and search optimization for effective few-shot cognitive predictions in gaming scenarios.

Findings

Outperforms traditional models in Wasserstein-1 distance, MSE, and accuracy.

Effective in small-group cognitive simulation with minimal data.

Enhances tailored game design for cognitive development.

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.