English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We conducted an experiment of a visuomotor tracking task undertaken by human participants and compared it with numerical simulations of the same task performed by a nonlinear stochastic model comprising additive and multiplicative white Gaussian noise, state feedback terms, and a deadband function. We identified the model parameters using particle swarm optimization to minimize squared residuals between the probability density functions (PDFs) of the human and those of the model. All experimentally obtained PDFs were in close agreement with those simulated by the model. We finally propose a reduced model for system identification in order to decrease the number of model parameters and demonstrate that it also reproduces accurate PDFs without prior knowledge of an experimental system.

Stochastic Dynamic Modeling of Human Visuomotor Tracking Task of an Unstable Virtual Object