Curriculum effects in multitask learning through the lens of contextual inference

When learning multiple tasks, the structure of practice, or curriculum, profoundly influences learning outcomes across domains, including motor learning, rule learning, perceptual learning, and machine learning. In multitask learning settings, there is often a trade-off between the speed of acquisition and long-term retention. For example, in motor learning, acquisition appears faster, but retention is substantially reduced with blocked training compared to randomly interleaved training. In machine learning, this effect is known as catastrophic forgetting. In contrast, perceptual and cognitive learning benefit from structured, predictable curricula such as blocked training. We propose contextual inference as a unifying framework to explain these effects, emphasizing the integration of task transition dynamics, contextual cues and observation noise during learning. Insights from this framework may allow mitigating catastrophic interference in machine learning by leveraging principles inspired by biological learning.