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Updated: Jun 14, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

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Predictive Representations: Building Blocks of Intelligence.

Wilka Carvalho¹, Momchil S Tomov^2,3, William de Cothi⁴

¹Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02134, U.S.A. wcarvalho92@gmail.com.

Neural Computation

|August 30, 2024

Summary

This summary is machine-generated.

Predictive representations, like the successor representation, are crucial for adaptive behavior and intelligence. This review connects reinforcement learning theory with cognitive neuroscience, highlighting their role in brain function.

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Last Updated: Jun 14, 2025

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Reinforcement Learning

Background:

Adaptive behavior relies on predicting future events.
Reinforcement learning theory provides a framework for understanding useful predictive representations and their computation.
Existing research spans both engineering applications and cognitive/neuroscientific models.

Purpose of the Study:

To review and integrate theoretical concepts from reinforcement learning with empirical findings in cognition and neuroscience.
To highlight the role of predictive representations, particularly the successor representation, in intelligent systems and brain function.

Main Methods:

Literature review integrating reinforcement learning theory with cognitive and neuroscience research.
Focus on the successor representation and its generalizations.

Main Results:

The successor representation and its extensions are identified as key predictive representations.
These representations serve dual roles as engineering tools and models of brain function.
A convergence between theoretical and empirical work is observed.

Conclusions:

Specific types of predictive representations, exemplified by the successor representation, are fundamental to intelligence.
These representations act as versatile building blocks for intelligent behavior.
The integration of reinforcement learning with neuroscience offers a unified perspective on predictive processing.