Year: 2026
Type: preprint
Abstract: Implicit adaptation operates automatically to ensure that well-learned movements remain exquisitely calibrated. A long-standing puzzle is how this process is applied when learning a new skill: Prior work has highlighted that implicit recalibration is inflexible, even when its operation results in maladaptive behavior given the sensorimotor mapping required for the new skill. Here, we revisited this problem, training participants for up to a month on a range of tasks with novel mappings, including mirror reversals and large rotations. Contrary to previous findings, implicit recalibration was flexibly reconfigured after prolonged training in a manner appropriate for each novel mapping. Training that emphasized feedforward planning rather than feedback control was sufficient to induce this reconfiguration. Learning involved reconfiguration of the sensory predictions and error correction policy for the new mapping, and participants were able to switch between mappings with minimal interference. These results demonstrate that implicit recalibration is reconfigured during skill acquisition, enabling flexible, context-specific adaptation.
Institutions: Princeton University, University of California, Berkeley
Language: English
Topic: Motor Control and Adaptation
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: National Institutes of Health
OpenAlex ID: W7225186430