Ma Shumeng, Jia Ning
College of Education, Hebei Normal University, Shijiazhuang 050025, China.
J Intell. 2024 Nov 13;12(11):116. doi: 10.3390/jintelligence12110116.
Extended testing time in Raven's Progressive Matrices (RPM) can lead to increased fatigue and reduced motivation, which may impair cognitive task performance. This study explores the application of artificial intelligence (AI) in RPM by combining eye-tracking technology with machine learning (ML) models, aiming to explore new methods for improving the efficiency of RPM testing and to identify the key metrics involved. Using eye-tracking metrics as features, ten ML models were trained, with the XGBoost model demonstrating superior performance. Notably, we further refined the period of interest and reduced the number of metrics, achieving strong performance, with accuracy, precision, and recall all above 0.8, using only 60% of the response time and nine eye-tracking metrics. This study also examines the role of several key metrics in RPM and offers valuable insights for future research.
在瑞文标准推理测验（RPM）中延长测试时间会导致疲劳加剧和动机降低，这可能会损害认知任务表现。本研究通过将眼动追踪技术与机器学习（ML）模型相结合，探索人工智能（AI）在RPM中的应用，旨在探索提高RPM测试效率的新方法，并确定其中涉及的关键指标。以眼动追踪指标作为特征，训练了十个ML模型，其中XGBoost模型表现出卓越性能。值得注意的是，我们进一步细化了感兴趣的时间段并减少了指标数量，仅使用60%的反应时间和九个眼动追踪指标就实现了出色的性能，准确率、精确率和召回率均高于0.8。本研究还考察了几个关键指标在RPM中的作用，并为未来研究提供了有价值的见解。
