A Test of Fitts' Law in a Dual-Task Paradigm

Abstract

A simulated automobile driving environment was used to assess the validity of Fitts' Law under dual-task conditions. An aimed hand movement task was used as the Fitts task representative of reaching for controls on an instrument panel. The task required activation of one of four touch-sensitive response plates upon recognition of an auditory stimulus. Movement difficulty was manipulated by varying target location and size. Target location was examined at four levels corresponding to position in a 2 × 2 array. Distances of the targets from the two-o'clock position on the steering wheel ranged from 27 cm to 53 cm. The target plates were square and measured 1.27 cm (1/2 inch) or 0.64 cm (1/4 inch) along the side. The eight combinations of movement amplitude and target size yielded seven unique levels of Fitts' Index of Difficulty (ID) ranging from 5.4 to 7.4. The movement task was performed alone and in combination with two other tasks to create three levels of task loading. A display monitoring task was used to represent the visual demands of driving while an unstable tracking task was used to represent the perceptual-motor demands of driving. Following adequate training, ten subjects performed three replications of six task conditions (three loading levels x two target sizes). Within each replication, the order of testing was counterbalanced across subjects. The dual-task visual loading condition involving the movement and monitoring tasks consistently resulted in the longest reaction times. The dual-task perceptual-motor loading condition involving the movement and tracking tasks resulted in consistently longer movement times. Fitts' ID had a significant effect on both reaction time and movement time for all three conditions of task loading. However, separate linear regressions of movement time on ID for each task loading level resulted in R2 values of 0.66 to 0.82. Multiple linear regressions involving target size and movement amplitude as predictor variables provided better predictions with R2 values of 0.90 to 0.93. The regression equations provided in this paper may be used by designers to estimate differences in response time due to control size and location.