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Signatures of reinforcement learning in natural behavior
Catherine A Hartley1,2, Susan L Benear1, Aaron S Heller3
1New York University, Department of Psychology.
Current Directions in Psychological Science
|October 30, 2025
Summary
Reinforcement learning algorithms explain how agents learn rewarding choices from experience. This study extends these models to better understand learning in complex, natural environments and everyday human behaviors.
Area of Science:
- Cognitive Science
- Neuroscience
- Machine Learning
Background:
- Humans learn to achieve desirable outcomes through experience.
- Reinforcement learning (RL) provides formal algorithms for reward-guided learning.
- Current RL models effectively explain behavior in controlled lab settings.
Purpose of the Study:
- To extend reinforcement learning (RL) constructs and algorithms.
- To account for learning in complex, natural environments.
- To bridge the gap between RL theory and real-world human behavior.
Main Methods:
- Operationalizing RL constructs (states, actions, rewards) for naturalistic settings.
- Reviewing empirical studies demonstrating RL signatures in everyday behaviors.
- Analyzing how RL algorithms apply to complex, real-world learning challenges.
Main Results:
- Reinforcement learning principles are applicable beyond controlled environments.
- Evidence suggests RL signatures in diverse human behaviors in natural settings.
- RL models can be extended to explain real-world learning.
Conclusions:
- Reinforcement learning (RL) offers a powerful framework for understanding human learning.
- RL constructs and algorithms can be adapted for complex, natural environments.
- Future research should explore RL's role in everyday decision-making and behavior.
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