Parameterized Estimation of Long-Range Correlation and Variance Components in Human Serial Interval Production

in Motor Control
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Repetitive movements lead to isochronous serial interval production which exhibit inherent variability. The Wing-Kristofferson model offers a decomposition of the interresponse intervals in tapping tasks based on a cognitive component and on a motor component. We suggest a new theoretical and fully parametric approach to this model in which the cognitive component is modeled as a long-memory process and the motor component is treated as a white noise process, mutually independent. Under these assumptions, we obtained the autocorrelation function and the spectral density function. Furthermore, we propose an estimator based on the maximization of the frequency-domain representation of the likelihood function. Finally, we conducted a simulation study to assess the properties of this estimator and performed an experimental study involving tapping tasks with two target frequencies (1.250 Hz and 0.625 Hz).

Diniz and Barreiros are with CIPER, Faculty of Human Kinetics, Technical University of Lisbon, Lisbon, Portugal. Crato is with CEMAPRE, Institute of Economics and Management, Technical University of Lisbon, Lisbon, Portugal.

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