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 analyse 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 modelling approach using probabilistic dimensionality reduction, we investigate covariation across learning trajectories with few assumptions about learning curve form or relationships between tasks. Modelling results show substantial 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.

学习各种任务的灵活性是人类认知的标志。为了增进我们对个体差异和跨任务学习动态的理解，我们分析了36297名个体在Lumosity在线认知培训平台上学习了51种不同任务时的学习轨迹的潜在结构。通过使用概率降维的数据驱动建模方法，我们研究了学习轨迹之间的协方差，而很少假设学习曲线的形式或任务之间的关系。建模结果表明跨任务存在实质性的协变，因此可以通过观察其他任务的轨迹来预测完全未观察到的学习轨迹。