Cheng-Jhe Lin1, Changxu Wu. 1. Industrial and Systems Engineering, State University of New York at Buffalo, Buffalo, NY, USA.
Abstract
Numerical typing is an important perceptual-motor task whose performance may vary with different pacing, finger strategies and urgency of situations. Queuing network-model human processor (QN-MHP), a computational architecture, allows performance of perceptual-motor tasks to be modelled mathematically. The current study enhanced QN-MHP with a top-down control mechanism, a close-loop movement control and a finger-related motor control mechanism to account for task interference, endpoint reduction, and force deficit, respectively. The model also incorporated neuromotor noise theory to quantify endpoint variability in typing. The model predictions of typing speed and accuracy were validated with Lin and Wu's (2011) experimental results. The resultant root-mean-squared errors were 3.68% with a correlation of 95.55% for response time, and 35.10% with a correlation of 96.52% for typing accuracy. The model can be applied to provide optimal speech rates for voice synthesis and keyboard designs in different numerical typing situations. PRACTITIONER SUMMARY: An enhanced QN-MHP model was proposed in the study to mathematically account for the effects of pacing, finger strategies and internalised urgency on numerical typing performance. The model can be used to provide optimal pacing for voice synthesise systems and suggested optimal numerical keyboard designs under urgency.
Numerical typing is an important perceptual-motor task whose performance may vary with different pacing, finger strategies and urgency of situations. Queuing network-model human processor (QN-MHP), a computational architecture, allows performance of perceptual-motor tasks to be modelled mathematically. The current study enhanced QN-MHP with a top-down control mechanism, a close-loop movement control and a finger-related motor control mechanism to account for task interference, endpoint reduction, and force deficit, respectively. The model also incorporated neuromotor noise theory to quantify endpoint variability in typing. The model predictions of typing speed and accuracy were validated with Lin and Wu's (2011) experimental results. The resultant root-mean-squared errors were 3.68% with a correlation of 95.55% for response time, and 35.10% with a correlation of 96.52% for typing accuracy. The model can be applied to provide optimal speech rates for voice synthesis and keyboard designs in different numerical typing situations. PRACTITIONER SUMMARY: An enhanced QN-MHP model was proposed in the study to mathematically account for the effects of pacing, finger strategies and internalised urgency on numerical typing performance. The model can be used to provide optimal pacing for voice synthesise systems and suggested optimal numerical keyboard designs under urgency.