De novo motor learning as controller synthesis: a conceptual framework

Abstract

Most studies of motor learning focus on adaptation, which can be described as recalibration within an existing controller following changes in dynamics, kinematics, or sensory feedback. In these paradigms, control variables, coordinate representations, and feedback organization are treated as available a priori, and learning as parameter tuning within a specified controller architecture. However, de novo motor-learning tasks require learners to establish novel relationships between intention, sensory feedback, and action, rather than merely recalibrating a familiar control organization. These tasks often show slow or variable acquisition, selective and task-dependent 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 or variable acquisition, structured generalization, context dependence, and learning-rate/outcome dissociations as possible consequences of controller-memory formation, stabilization, retrieval, and expression, rather than of slow parameter convergence within an established controller. It also suggests tests for distinguishing controller synthesis from fixed-structure alternatives based on adaptation, contextual inference, or policy learning.