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Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

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Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective.

Matthew M Botvinick¹, Yael Niv¹, Andew G Barto²

¹Princeton Neuroscience Institute, Department of Psychology, Princeton University, Green Hall, Princeton, NJ 08540, United States.

|October 18, 2008

Summary

Hierarchical reinforcement learning offers a new computational framework for understanding how behavior is structured into tasks and subtasks. This approach may explain the neural basis of complex actions and skill acquisition in the brain.

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Area of Science:

Neuroscience
Cognitive Science
Artificial Intelligence

Background:

Behavior is widely recognized as hierarchical, composed of tasks, subtasks, and actions.
The prefrontal cortex is traditionally linked to the neural basis of behavioral hierarchy.

Purpose of the Study:

To reexamine behavioral hierarchy and its neural substrates using computational reinforcement learning.
To explore how hierarchical reinforcement learning models map onto prefrontal cortex structures.

Main Methods:

Utilizing hierarchical reinforcement learning (HRL) models.
Analyzing the aggregation of actions into reusable subroutines or skills within HRL.

Main Results:

HRL provides a framework for understanding how agents learn reusable skills.
Specific HRL components may map to dorsolateral and orbital prefrontal cortex regions.

Conclusions:

Hierarchical reinforcement learning offers a computational complement to psychological models of behavior.
HRL provides a framework for investigating the computational and neural underpinnings of hierarchically structured behavior.