Abstract
With age there are reductions in sensory, attentional, and motor function that would predict deficits in performance in older drivers. A variety of studies suggest that the magnitude of these effects varies with the attentional demands of the task: age-deficits in performance are especially notable in tasks where there is high attentional load. These studies typically manipulate attentional load by imposing a secondary task that does not go naturally with driving (e.g. mental arithmetic). In this study, an attempt was made to manipulate the demands of the drive by using challenge factors intrinsic to driving. Three manipulations were investigated: a sensory challenge (driving in fog as compared to driving on a clear day); a traffic density challenge (driving in high as compared to low density traffic); and a navigational challenge (having to use memorized directions, signs and landmarks to navigate while driving as compared to simply “following the road”). The effects of these manipulations were investigated alone and in combination in 19 older adults (M age = 70.8 years) and 21 younger adults (M age = 18.2 years). Participants were tested in a high fidelity driving simulator. Hazard RT, collisions, steering performance and navigational errors were measured. Contrary to prediction, when the driving task was made more challenging, the older drivers performed as well or better than the younger adults, with significantly fewer collisions and marginally lower hazard RT. This high level of performance may have arisen because older drivers adjusted their speeds more appropriately in the face of different driving challenges. Speed adjustment indices were calculated for each condition and participant. For the older adults, these speed adjustment indices correlated with measures of selective and divided attention, which suggests that older adults with deficits in attentional processing adjust their driving speeds to compensate.
Natural Sciences & Engineering Research Council of Canada, Ontario Neurotrauma Foundation, Auto21: Network Centres of Excellence, Canadian Foundation for Innovation.