HMN 2026: how Human brain separates goals and uncertainty to enable adaptive decision-making

Humans possess a remarkable balance between stability and flexibility, enabling them to quickly establish new plans and adjust goals even in the face of sudden changes. However, “model-free reinforcement learning,” which is widely used in robotics and exemplified by AlphaGo’s famous match against Lee Sedol, struggles to achieve these two capabilities simultaneously.

KAIST’s research team has discovered that the secret lies in the unique information processing method within the prefrontal cortex, a principle that could serve as the foundation for developing brain-like AI that is both flexible and stable.

The research team led by Professor Sang Wan Lee from the Department of Brain and Cognitive Sciences, in collaboration with IBM AI Research, has deciphered how the human brain manages goal changes in uncertain situations, suggesting a new direction for next-generation reinforcement learning.

This study featured Ph.D. candidate Yoondo Sung as the first author and Dr. Mattia Rigotti of IBM AI Research as the second author, with Professor Sang Wan Lee serving as the corresponding author. The research results were published in the journal Nature Communications.

The research team highlighted a critical limitation of current reinforcement learning models: They lose the balance between flexibility for goal pursuit and stability in uncertain environments. Humans, however, achieve both simultaneously. The team hypothesized that this difference arises from how the prefrontal cortex represents information.

Using functional MRI (fMRI) experiments, reinforcement learning models, and advanced AI analyses, the team revealed that the human prefrontal cortex has a unique embedding structure that represents goal information and uncertainty information separately to prevent interference. Individuals with more distinct separation between these channels were able to adapt strategies when goals shifted, while maintaining stable judgment despite environmental uncertainty.

The team likened this mechanism to multiplexing in communication technology, where multiple signals are transmitted simultaneously without interference.

In this way, the human prefrontal cortex operates through two channels: one that sensitively tracks goal changes to ensure flexibility in decision-making, and another that isolates environmental uncertainty to maintain stable judgment.

An interesting point is that the prefrontal cortex goes beyond simply executing control guided by the first channel; it uses the second channel to actually choose which learning strategy to use depending on the situation.

This demonstrates the brain’s meta-learning capabilities, meaning it learns not only what to learn but also how to learn—by choosing which learning strategy to use. This is why humans remain resilient in constantly changing situations.

The implications of this research extend across various fields, including the analysis of individual reinforcement and meta-learning abilities, personalized education design, cognitive diagnosis, and human-computer interaction (HCI). Moreover, embedding brain-inspired representation structures into AI could lead to brain-like thinking AI—allowing AI to better understand human intentions and values, reducing dangerous judgments, and enabling safer cooperation with humans.

Lead researcher Professor Sang Wan Lee emphasized the significance of the findings: “This study clarifies the brain’s fundamental operating principles—from flexibly following changing goals to stably establishing plans—from an AI perspective. These principles will serve as a core foundation for next-generation AI, allowing it to adapt like a human and learn more safely and intelligently.”

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