Touchscreen typing as optimal supervisory control

Traditionally, touchscreen typing has been studied in terms of motor performance. However, recent research has exposed a decisive role of visual attention being shared between the keyboard and the text area. Strategies for this are known to adapt to the task, design, and user. In this paper, we propose a unifying account of touchscreen typing, regarding it as optimal supervisory control. Under this theory, rules for controlling visuo-motor resources are learned via exploration in pursuit of maximal typing performance. The paper outlines the control problem and explains how visual and motor limitations afect it. We then present a model, implemented via reinforcement learning, that simulates co-ordination of eye and fnger movements. Comparison with human data affrms that the model creates realistic fnger-and eye-movement patterns and shows human-like adaptation. We demonstrate the model's utility for interface development in evaluating touchscreen keyboard designs.