Primate-like perceptual decision making emerges through deep recurrent reinforcement learning

2026-01-18

AI Summary

This study investigates the evolutionary origins of perceptual decision-making mechanisms in primates, focusing on their capacity to optimize reward under noisy and time-varying sensory information. Using end-to-end deep recurrent reinforcement learning, we trained neural networks to perform a noisy perceptual discrimination task and, for the first time in a computational model, successfully reproduced key primate decision-making characteristics—including speed–accuracy tradeoffs and flexible changes of mind. The internal dynamics of the trained models closely align with neurophysiological observations, demonstrating that such decision strategies can spontaneously emerge within a reinforcement learning framework. These findings provide direct computational evidence that perceptual decision mechanisms may have been shaped by evolutionary pressures to maximize reward in uncertain environments.

Technology Category

• Machine Learning: Evolutionary Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

Progress has led to a detailed understanding of the neural mechanisms that underlie decision making in primates. However, less is known about why such mechanisms are present in the first place. Theory suggests that primate decision making mechanisms, and their resultant behavioral abilities, emerged to maximize reward in the face of noisy, temporally evolving information. To test this theory, we trained an end-to-end deep recurrent neural network using reinforcement learning on a noisy perceptual discrimination task. Networks learned several key abilities of primate-like decision making including trading off speed for accuracy, and flexibly changing their mind in the face of new information. Internal dynamics of these networks suggest that these abilities were supported by similar decision mechanisms as those observed in primate neurophysiological studies. These results provide experimental support for key pressures that gave rise to the primate ability to make flexible decisions.

Problem

• perceptual decision making
• primate
• reinforcement learning
• neural mechanisms
• evolutionary pressure

Innovation

• deep recurrent reinforcement learning
• perceptual decision making
• neural mechanisms
• speed-accuracy tradeoff
• flexible decision-making