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Curriculum effects in multitask learning through the lens of contextual inference
Sabyasachi Shivkumar1, Máté Lengyel2, Daniel M Wolpert3
1Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Kavli Institute for Brain Science, Columbia University, New York, USA.
Current Opinion in Neurobiology
|October 10, 2025
Summary
Practice structure significantly impacts learning across motor, rule, perceptual, and machine learning tasks. Contextual inference offers a unified framework to explain trade-offs between learning speed and retention, potentially mitigating catastrophic forgetting in AI.
Area of Science:
- Cognitive Science
- Machine Learning
- Neuroscience
Background:
- The structure of practice, or curriculum, is crucial for multitask learning outcomes.
- A trade-off often exists between rapid learning acquisition and long-term retention in multitask settings.
- Blocked training can enhance acquisition but reduce retention (e.g., motor learning), while interleaved training may improve retention but slow acquisition.
Purpose of the Study:
- To propose a unifying framework, contextual inference, to explain the divergent effects of practice structures on learning across different domains.
- To investigate how task transition dynamics, contextual cues, and observation noise influence learning outcomes.
- To explore how principles from biological learning can inform machine learning to mitigate catastrophic interference.
Main Methods:
- The study proposes a theoretical framework, contextual inference, integrating task transition dynamics, contextual cues, and observation noise.
- It draws parallels between findings in motor learning, perceptual learning, cognitive learning, and machine learning (catastrophic forgetting).
- The framework aims to explain why blocked training benefits some learning types while harming others.
Main Results:
- Blocked training can lead to faster acquisition but poorer long-term retention in domains like motor learning.
- Perceptual and cognitive learning often benefit from structured, blocked training curricula.
- Machine learning exhibits 'catastrophic forgetting,' a phenomenon analogous to reduced retention in blocked motor learning.
Conclusions:
- Contextual inference provides a unified explanation for the impact of practice structure on learning across diverse domains.
- Understanding these principles can inspire methods to reduce catastrophic interference in machine learning.
- Leveraging insights from biological learning may enhance the robustness and efficiency of artificial intelligence systems.
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