Multidimensional Bayesian Active Machine Learning of Working Memory Task Performance
The authors validated a Bayesian, two-axis active classification approach for working memory tasks using an immersive virtual environment. They tested how well this method can estimate performance across spatial load (L) and feature-binding load (K). The results show the two-axis method recovers thresholds comparable to traditional methods while providing a richer understanding of performance boundaries.