Pointing tasks in human–computer interaction obey certain speed–accuracy tradeoff rules. In general, the more accurate the task to be accomplished, the longer it takes and vice versa. Fitts’ law models the speed–accuracy tradeoff effect in pointing as imposed by the
task parameters, through Fitts’ index of difficulty (
Id) based on the ratio of the nominal movement distance and the size of the target. Operating with different speed or accuracy biases, performers may utilize more or less area than the target specifies, introducing another subjective layer of speed–accuracy tradeoff relative to the task specification. A conventional approach to overcome the impact of the subjective layer of speed–accuracy tradeoff is to use the a posteriori “effective” pointing precision
We in lieu of the nominal target width
W. Such an approach has lacked a theoretical or empirical foundation. This study investigates the nature and the relationship of the two layers of speed–accuracy tradeoff by systematically controlling both
Id and the index of target utilization
Iu in a set of four experiments. Their results show that the impacts of the two layers of speed–accuracy tradeoff are not fundamentally equivalent. The use of
We could indeed compensate for the difference in target utilization, but not completely. More logical Fitts’ law parameter estimates can be obtained by the
We adjustment, although its use also lowers the correlation between pointing time and the index of difficulty. The study also shows the complex interaction effect between
Id and
Iu, suggesting that a simple and complete model accommodating both layers of speed–accuracy tradeoff may not exist.
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