Comparison of sequential data analysis and functional data analysis for locomotor adaptation - Torin Quinlivan; Kacy Kane; Christopher M. Hill; Duchwan Ryu (PLoS ONE, 2025)
Learning rates for skills such as walking may depend on circumstances or time, while incentivization with punishments or rewards may affect human skill learning. We consider a state space model for dynamically changed learning rates and figure out the effect of incentivization on the learning rates by utilizing a dynamically weighted particle filter. However, estimations of model parameters, including the learning rate, require a demanding computational burden, especially when the data are collected over a long period. To overcome computational difficulty, we utilize an efficient sequential Monte Carlo method, dynamically weighted particle filter, in the estimations of model parameters. Alternatively, we consider a functional data analysis for the learning rates and the effect of the incentivization. Two approaches have led to reasonable estimations of learning rates. We present the estimated learning rates and the effect of incentivization on the learning rates from two approaches, as well as the comparisons of their results.