Inferring latent learning factors in large-scale cognitive training data

The flexibility to learn diverse tasks is a hallmark of human cognition. To improve our understanding of individual differences and dynamics of learning across tasks, we analyze the latent structure of learning trajectories from 36,297 individuals as they learned 51 different tasks on the Lumosity online cognitive training platform. Through a data-driven modeling approach using probabilistic dimensionality reduction, we investigate covariation across learning trajectories with few assumptions about learning curve form or relationships between tasks. Modeling results show significant covariation across tasks such that an entirely unobserved learning trajectory can be predicted by observing trajectories on other tasks. The latent learning factors from the model include a general ability factor that is expressed mostly at later stages of practice, and additional task-specific factors that carry information capable of accounting for manually defined task features and task domains such as attention, spatial processing, language and math.