Adaptive Decision-Making in Biological and Artificial Systems
A Neuroscientific Comparison of Learning Under Uncertainty
Abstract
This manuscript compares biological and artificial learning systems through the lens of adaptive decision-making under uncertainty. It examines how humans and intelligent systems perceive information, represent features, infer patterns, select policies, act, receive feedback, and adjust future behavior. The comparison emphasizes both parallels and limits, including metacognition, affective processing, embodied cognition, and neurobiological constraints in humans, alongside model inference, optimization, and reinforcement learning mechanisms in artificial systems.
Keywords
Artificial intelligence, neuroscience, reinforcement learning, decision-making, uncertainty, human learning, machine learning, cognitive science.
Availability
The full manuscript is not publicly accessible at this time. Citation details and full text will be added after publication or approved release.