Attention Function Structure of Older and Younger Adult Drivers

Groups of younger (n=49, M age = 21.7 years) and older (n=52, M age = 73.0 years) adults performed computer-based cognitive tests and simulated driving. Results from the cognitive tests were submitted to Principal Components Analysis (PCA) and 6 components were extracted that explained more than 77% of the variance. The components were labeled speed, divided, sustained, executive, selective/inhibition, and visual search in descending order of amount of variance explained. The component scores were used to predict simulated driving performance. Hierarchical step-wise regressions were computed with driving performance as the criterion, and age group (forced) and the component scores (step-wise) as predictors. Results showed that the speed and divided components were more likely to explain additional driving performance variance beyond age group than the other components

The Effect of Age on Decision Making During Unprotected Turns Across Oncoming Traffic

The present study examined whether age-related differences in quantitative measures of left-tum performance could explain older drivers\u27 increased susceptibility to crashing while making unprotected left turns across traffic. Older and younger adults made left turns across traffic in a driving simulator. Time to decide to turn, time to negotiate the turn, the size of the accepted gap, gap clearance, and time to collision with an oncoming vehicle were measured. Significant effects of age were found in decision time, turn time and gap size. A significant interaction between age group and the speed of oncoming traffic was obtained for decision time. Implications for older adult\u27s safety and future directions are discussed

Deciphering Psychological-Physiological Mappings While Driving and Performing a Secondary Memory Task

An autonomic space model of sympathetic and parasympathetic influences on the heart has been proposed as a method of deciphering psychological-physiological mappings for driving-related tasks. In the current study, we explore the utility of the autonomic space model for deciphering mappings in a driving simulation environment by comparing a single-task driving-only condition to two dual-task, driving-with-a-secondary-workingmemory task conditions. Although limited by a small sample size, the results illustrate the advantages physiological measures can have over performance measures for detecting changes in the psychological process required for drivingrelated task performance. Future research will include a repetition of this same study with more subjects as well the collection of on-the-road autonomic nervous system data

An Examination of the Relationship Between Attention Profiles and Simulated Driving Performance

This study examined whether attention profiles from a computerized test battery relate to simulated driving performance. Five attention abilities were examined in the study: sustained, divided, selective, switching, and scanning. Participants completed eight tasks in a computer-based test battery and four driving scenarios designed to tap the same attention abilities. Physiological measures were collected during the test battery and the driving scenarios. Principal components analysis (PCA) with varimax rotation extracted seven components from the test battery, including the five proposed abilities along with speed and orienting components. Component scores were used as predictors of simulated driving performance in stepwise regressions and explained a significant proportion of variance (ranging from 7% - 26%) for most measures of driving performance. The speed, visual search, and divided attention components appeared as significant predictors more often than did the sustained, switching, orienting, and selective components. When physiological measures were added to the regressions, they explained additional variance beyond that explained by the component scores, but there was no consistent relation between simulated driving performance and any particular physiological measure

Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving

We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the SEEV model. Here we compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R2 = 0.88) and novice drivers (R2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. We concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers

Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving

We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the SEEV model. Here we compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R2 = 0.88) and novice drivers (R2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. We concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers