Mathematically modelling the effects of pacing, finger strategies and urgency on numerical typing performance with queuing network model human processor

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.

Keywords:

• cognitive modelling
• numerical typing
• numerical keyboard design
• typing error

Acknowledgements

This research is partially funded by a National Science Foundation (NSF) grant. The authors would like to thank an anonymous English speaker for her help with the preparation of the manuscript.

Notes

1. Some experimental studies found ballistic movement variability was proportional to either movement amplitude (e.g. Lin and Drury 2011) or squared movement amplitude (e.g. Beggs et al. 1972). The ballistic movements in their experiments were either controlled in movement time by a metronome or performed without emphasis on rapid responses. Since the rapid typing movements were encouraged in both Lin and Wu (2011) as well as in van Beers et al. (2004) but their time were not controlled, the results in those experiments might not apply to the current modelling work.

2. The root-mean-squared error percentage is computed by:

 where E_j and M_j were experimental observation and model prediction in condition j, respectively, n was total number of observations/predictions to be compared.