A new study has revealed striking similarities between human learning and artificial intelligence (AI) learning, shedding light on how people process knowledge and how AI systems can be designed to think more intuitively. Conducted by researchers at Brown University and published in the Proceedings of the National Academy of Sciences, the research compares two fundamental modes of learning and how they operate in both humans and AI.
Humans typically learn through two approaches: in-context learning and incremental learning. In-context learning allows for fast adaptation, such as understanding the rules of a new board game after seeing just a few examples. Incremental learning, on the other hand, builds knowledge gradually—similar to practicing a song on the piano until it becomes second nature. According to lead researcher Jake Russin, these approaches mirror the functions of working memory and long-term memory in the brain.
To test the parallels, Russin and his team trained AI models using meta-learning, a method that allows systems to “learn how to learn.” When exposed to thousands of tasks, the AI began demonstrating in-context learning abilities—successfully recombining known concepts into new solutions, such as recognizing a “green giraffe” despite never having seen that combination before. This finding suggests that AI develops flexibility only after a foundation of incremental learning, much like humans who can quickly grasp rules after extensive prior experience.
The research also revealed important trade-offs between flexibility and retention. Just like people, AI tends to remember difficult tasks better because errors trigger stronger memory encoding. Conversely, tasks learned too easily enhance short-term flexibility but do not engage long-term memory as effectively. As professor Michael Frank explained, this insight aligns with what neuroscientists know about human cognition and shows that studying neural networks can provide fresh clues about the brain.
Beyond theory, the implications for AI development are profound. The team highlighted that future intuitive AI tools—particularly in sensitive domains like healthcare and mental health—must account for these human-like learning dynamics. By designing systems that balance flexibility with retention, researchers could create AI assistants that are not only smarter but also more trustworthy and aligned with human cognitive processes.
Conclusion
This study demonstrates that human cognition and AI learning share deeper parallels than previously understood. By leveraging these similarities, scientists can both enhance our understanding of the brain and build next-generation AI systems that behave in more natural, human-like ways. The intersection of neuroscience and machine learning may hold the key to developing technology that is not only powerful but also intuitive, reliable, and ethical.
