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Pavlovian Conditioned Approach Training in Rats

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Published on: February 4, 2016

Rational and mechanistic perspectives on reinforcement learning.

¹Division of Psychology and Language Sciences, Centre for Economic Learning and Social Evolution (ELSE), UCL, London, WC1E 6BT, United Kingdom.

|August 30, 2008

Summary

This study explores reinforcement learning (RL) in cognitive and brain sciences, differentiating between mechanistic and rational levels of analysis. It argues for prioritizing the rational perspective in RL research unless mechanistic evidence is compelling.

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

Reinforcement learning (RL) models are increasingly used to understand neural and cognitive functions.
RL can be conceptualized at two distinct levels: mechanistic (how the brain/agent operates) and rational (optimal learning strategies).
Current research often implicitly favors a mechanistic interpretation of RL in cognitive and brain sciences.

Purpose of the Study:

To differentiate between mechanistic and rational perspectives of reinforcement learning.
To examine the types of evidence that distinguish between these two levels of description.
To propose a shift towards a rational-level interpretation of RL in cognitive and brain sciences.

Main Methods:

Conceptual analysis of reinforcement learning frameworks.
Review of existing literature in cognitive and brain sciences applying RL.
Argumentation for prioritizing rational-level explanations.

Main Results:

Reinforcement learning can be understood as either a description of cognitive/neural mechanisms or as a set of rational learning principles.
Distinguishing between these levels requires specific types of empirical evidence.
The paper advocates for the rational perspective as the default interpretation.

Conclusions:

Accounts of reinforcement learning should be framed at the rational level by default.
A mechanistic interpretation should only be adopted when strongly supported by evidence.
This viewpoint has significant implications for developing reinforcement-based theories in cognitive and brain sciences.