Introduction: Motor adaptation is the process of adapting a movement plan to unexpected results due to a changing environment or changes in physical performance. Using the theory of optimal forward control this process can be described using using a forward control model with two learning parameters and two noise factors. This study will use Bayesian inference and a No-U-Turn sampler to estimate these learning and noise parameters from movement data from a reaching task in a small (N=60) and a large dataset (N=2226).
Methods: In total, six models were created and tested following a state-space model for adaptation. Three models used the same hierarchical design for the learning parameters and compared different hierarchical approaches for priors for planning noise (σ_η and execution noise (σ_ϵ). The other three models used the same non-hierarchical design for the noise parameters and compared different hierarchical hyperpriors and a non-hierarchical design for the learning rate (A) and adaptation rate (B).
Results: For the smaller dataset, the same issue was seen for all model designs, where the posterior distributions of σ_η are heavily skewed toward 0, with the HMC unable to converge (^R > 1.1). For the large dataset a non-hierarchical approach for both the learning parameters and noise parameters was able to converge for the majority of subjects for all four parameters.
Discussion: While none of the models performed perfectly, in this paper a model is created that is able to quantify motor adaptation and motor noise from visuomotor task data in a large dataset of 2226 subjects. In the future this model can be used for further research into neurological and genetic factors that influence motor adaptation.