Modeling Human Visuomotor Adaptation with a Disturbance Observer Framework

Visuomotor adaptation studies have revealed a range of behaviors, including nonlinear saturation effects, instruction-dependent differences in performance, and distinct explicit and implicit components of adaptation. However, existing computational models have struggled to account for the full breadth of these findings. In this paper, we aim to address this gap by (1) designing a set of experiments to investigate the role of nonlinear saturation under different instructional contexts, and (2) introducing an abstract discrete-time model that captures plausible neural computations underlying the observed behaviors. The model draws on recent advances in control theory and is informed by prior work on oculomotor adaptation and cerebellar function, particularly within the floccular complex.