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The Computational and Neural Bases of Context-Dependent Learning
James B Heald1, Daniel M Wolpert1,2, Máté Lengyel2,3
1Department of Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; email: jamesbheald@gmail.com, wolpert@columbia.edu.
Annual Review of Neuroscience
|March 27, 2023
Summary
Contextual inference is crucial for flexible behavior and learning, especially when environmental context is uncertain. This framework unifies diverse brain research on context-dependent memory and learning.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- Flexible behavior relies on context-dependent memory creation, updating, and recall.
- Previous research often overlooked the challenge of contextual uncertainty in naturalistic settings.
Purpose of the Study:
- To present a theoretical framework for context-dependent learning under uncertainty.
- To identify core computations required for contextual inference.
Main Methods:
- Review of computational modeling approaches.
- Theoretical analysis of contextual uncertainty.
- Synthesis of experimental findings across multiple brain levels and regions.
Main Results:
- A formal approach to context-dependent learning with uncertainty is proposed.
- This framework organizes disparate experimental data on memory and learning.
- Key brain regions involved include the prefrontal cortex, hippocampus, and motor cortices.
Conclusions:
- Contextual inference is a fundamental computational challenge in learning.
- This framework provides a unified view of context-dependent learning.
- Contextual inference may be vital for understanding continual learning in the brain.
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