We use adaptive decoders to keep BCIs working as signals drift. But what if this convenience comes with a cost?

Every time the decoder adapts to accommodate neural changes, the brain gets feedback that its original pattern still works. Over time, this could create learned irrelevance—the neural equivalent of autopilot. Why explore new firing patterns when the decoder will compensate anyway?

If true, adaptive decoders might be optimizing for short-term stability at the expense of long-term learning. The brain stops adapting because the machine does the work.