A study from Saarland University and Max Planck Institute reveals that humans and large language models exhibit similar confusion patterns when processing deceptive code snippets, known as "atoms of confusion." Using fMRI and uncertainty metrics, researchers found aligned responses, suggesting ways to enhance AI coding tools. This could improve human-AI collaboration in software development.