Motor learning driven by sensory prediction errors is insensitive to task performance feedback

Accurate motor behavior relies on our ability to refine movements based on errors. Sensory prediction errors (SPEs), arising from discrepancies between expected and actual sensory feedback, are a primary driver of motor adaptation. Task performance errors (TPEs), reflecting failures to achieve movement goals, also influence learning. However, whether and how these errors interact to shape net learning, remains controversial. This controversy stems from difficulties in experimentally decorrelating SPEs and TPEs, ambiguity related to interpretations of task instructions, and inconsistencies between theory and computational models. To address this, we employed variants of an "error-clamp" adaptation paradigm across arm reaching experiments in humans of either sex. Addressing the ambiguity of whether the TPEs are ignored in standard clamp designs as assumed in theoretical (but not computational) models, experiment 1 manipulated TPE magnitude by varying the endpoint feedback location while holding SPE constant. We found that learning was uninfluenced by TPE size. Experiment 2 assumed that the TPE is in fact disregarded under clamp instructions. To then study SPE-TPE interactions, we induced TPEs of varying sizes by "jumping" the target while always clamping cursor feedback towards the original target. Instructions to reach in the direction of the new target also then induced an SPE. Crucially, learning driven by this SPE was unaffected by TPE magnitude, a result validated by two additional experiments. Together, these results provide converging evidence that SPE-mediated learning remains impervious to variations in task performance feedback, and point to a distinction in learning mechanisms triggered by these two error signals.Significance statement Humans improve their motor performance by learning from errors. This work aimed to resolve a long-standing controversy about how two error signals, task performance errors (TPEs) and sensory prediction errors (SPEs), interact during motor learning. Employing novel paradigms to isolate these errors, we demonstrate that SPE-driven learning is unaffected by variations in TPE magnitude. Our results challenge models proposing synergistic or competitive interactions between these two errors and suggest distinct mechanistic contributions of these error signals to motor learning.