Abstract
<p>Most studies of human motor learning focus on adaptation, in which an existing controller is recalibrated after changes in dynamics, kinematics, or sensory feedback. In these paradigms, relevant control variables, coordinate representations, and feedback organization are taken as available a priori, and learning is described as tuning parameters within an already specified controller architecture. However, de novo motor-learning tasks require learners to establish novel relationships between intention, sensory feedback, and action, rather than recalibrating a familiar controller. These tasks often show slow or variable acquisition, limited but structured generalization, strong context dependence, and dissociations between learning rate and final performance. Such features are difficult to explain solely as parameter tuning within a fixed controller architecture.Here we propose that de novo motor learning is better understood as controller synthesis, rather than only as parameter adaptation. On this view, learning involves forming and stabilizing content-addressable, plant-state-addressed controller memories: local sensorimotor control organizations that specify where a controller applies, which task-relevant signals are selected and routed, how state and progress are estimated, how control is computed, how internal control signals map onto action, and when and how the controller is expressed. This framework interprets slow acquisition, limited generalization, context dependence, and learning-rate/outcome dissociations as possible consequences of uncertainty about controller-memory structure, rather than slow parameter convergence within an established controller. It also suggests tests for distinguishing controller synthesis from fixed-structure adaptation, contextual inference, and policy-learning accounts.</p>