Many metrics have been used in an attempt to predict the effects of secondary tasks on driving behavior. Such metrics often give rise to seemingly paradoxical results, with one metric suggesting increased demand and another metric suggesting decreased demand for the same task. For example, for some tasks, drivers maintain their lane well yet detect events relatively poorly. For other tasks, drivers maintain their lane relatively poorly yet detect events relatively well. These seeming paradoxes are not time-accuracy trade-offs or experimental artifacts, because for other tasks, drivers do both well. The paradoxes are resolved if driver demand is modeled in two orthogonal dimensions rather than a single “driver workload” dimension. Principal components analysis (PCA) was applied to the published data from four simulator, track, and open road studies of visual-manual secondary task effects on driving. PCA reduced the task metrics to two underlying orthogonal components (hereafter, dimensions) which were consistent across studies, herein designated as physical and cognitive demand. Physical demand is associated with lateral and longitudinal driver performance (lane crossings, standard deviation of lateral position and speed), with correlated surrogate metrics of task time, step count, total glance time, number of glances, and subjective workload. Cognitive demand is associated with event detection (RT and miss rate), with correlated surrogate metrics of mean single glance time, long single glances, speed reduction, and task errors. The Dimensional Model of Driver Demand allows for a common simplified understanding of all these measures of visual-manual secondary task effects on driver performance.