The sensorimotor system is continuously adjusted to minimize error. Current theories assume that this
adaptation process entails the operation of multiple learning systems, with a key division between implicit
and explicit components. Recent studies have revealed several inconsistencies regarding the
characteristics and constraints of the implicit system, suggesting that the current framework is incomplete.
Here, we propose that these conflicting findings can be understood by recognizing that there are multiple
implicit subcomponents, distinguished by their distinct computational goals. One well-studied component
is implicit recalibration, a process critical for action execution which uses sensory-prediction errors to
automatically refine the sensorimotor map. Here we describe a second, novel component, implicit aiming,
a process which contributes to action selection to achieve the specific goals. Through a series of studies,
we find compelling evidence that those two implicit processes show a clear separation in their temporal
stabilities and contextual modulations. These distinct properties correspond to different computational
frameworks attributing learning dynamics to either contextual inference or cancellation of competing
neural populations, respectively. Together, these findings suggest an alternative framework for
sensorimotor adaptation based on the computational goals of the system rather than phenomenology.