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Predicting the Next Response: Demonstrating the Utility of Integrating Artificial Intelligence-Based Reinforcement
David J Cox1,2, Carlos Santos1
1Institute of Applied Behavioral Science at Endicott College, Beverly, MA USA.
Perspectives on Behavior Science
|June 16, 2025
Summary
Artificial intelligence (AI) reinforcement learning (RL) models, especially those incorporating punishment, significantly improve predictions of biological organism behavior. Combining AI with operant psychology enhances predictive accuracy and addresses theoretical questions.
Area of Science:
- Integrates artificial intelligence (AI) and behavioral science.
- Focuses on reinforcement learning (RL) and operant conditioning principles.
Background:
- Reinforcement and punishment concepts originate from psychology and AI.
- Psychology studies organism behavior; AI models agent behavior for reward maximization.
Purpose of the Study:
- To describe AI-based reinforcement learning (RL) characteristics and compare them to operant research.
- To explore how combining AI and operant insights can advance both fields.
- To demonstrate mutual utility by predicting biological organism responses.
Main Methods:
- Developed 12 artificial organisms (AOs) using operant research-informed feature sets, with and without punishment.
- Six participants predicted responses of these AOs.
- Introduced a 13th approach: human choice modeled by Q-learning for response prediction.
Main Results:
- Q-learning model achieved highest average predictive accuracy (95%).
- Models using molecular/molar information and punishment averaged 89% accuracy.
- Accuracy dropped significantly (47%-54%) without punishment contingencies.
Conclusions:
- AI-based RL techniques, combined with operant knowledge, enhance behavior prediction accuracy.
- This integration aids in addressing theoretical questions on multiscale behavior models and punishment's role in learning.
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