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Caching mechanisms for habit formation in Active Inference
D Maisto1, K Friston2, G Pezzulo3
1Institute for High Performance Computing and Networking, National Research Council, Via P. Castellino, 111, Naples 80131, Italy.
Neurocomputing
|February 15, 2020
Summary
This study introduces a computational model for habit formation using Active Inference, enabling agents to cache and reuse learned policies for efficiency. This approach explains habitual behaviors like perseveration and suggests multiple stages of habit development.
Area of Science:
- Computational Neuroscience
- Cognitive Science
- Machine Learning
Background:
- Distinction between deliberate and habitual control in learning.
- Organisms develop habits to save computational resources.
- Current artificial systems lack flexible control transfer seen in nature.
Purpose of the Study:
- To computationally implement habit formation within Active Inference.
- To model the transfer of control between deliberate and habitual modes.
- To explore the benefits and limitations of caching policies.
Main Methods:
- Utilized Active Inference, merging cybernetic theory and Bayesian inference.
- Developed a caching mechanism for policy probabilities in an Active Inference agent.
- Simulated three caching schemes to analyze computational benefits and limits.
Main Results:
- Habit formation explained by caching policy probabilities, leading to resource savings.
- Perseveration identified as a key aspect of habitual behavior.
- Identified multiple potential stages or kinds of habitual behavior based on caching schemes.
Conclusions:
- Active Inference provides a framework for modeling habit formation and control transfer.
- Caching policy probabilities offers computational benefits but has limitations.
- Habitual behavior may exist in various forms, influenced by caching strategies and context.
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