Comparison of sequential data analysis and functional data analysis for locomotor adaptation

Torin Quinlivan, Kacy Kane, Christopher M Hill and Duchwan Ryu

PLOS ONE, 2025, vol. 20, issue 8, 1-16

Abstract: 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.

Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0329940 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 29940&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0329940

DOI: 10.1371/journal.pone.0329940

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().