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

Attention Factors Compared to Other Predictors of Simulated Driving Performance Across Age Groups

Groups of young, middle-aged, and older adults performed a battery of computer-based attention tasks, the UFOV® and neuropsychological tests, and simulated low-speed driving in a suburban scenario. Results from the attention tasks were submitted to Maximum Likelihood factor analysis and 6 factors were extracted that explained more than 57% of the task variance. The factors were labeled speed, switching, visual search, executive, sustained, and divided attention in descending order of amount of task variance explained. The factor scores were used to predict simulated driving performance. Step-wise regressions were computed with driving performance as the criterion, and age, sex and the factor scores, the UFOV® scores, or the neuropsychological test scores as predictors. Results showed that the perceptual-motor speed and divided attention measures from the UFOV® and attention battery were more likely to explain driving performance variance than the neuropsychological tests

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

Assessment of the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving

We attempted to model attention allocation of experienced drivers using the SEEV model. Unlike previous attempts, the present work looked at attention to entities (vehicles, signs, traffic control devices) in the outside world rather than considering the outside world as a unitary construct. Model parameters were generated from rankings of entities by experienced drivers. Experienced drivers drove a scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Eye movements were monitored during the driving session. The results of fitting the observed eye movement data to our SEEV model were poor, and were no better than fitting the data to a randomized SEEV model. A number of explanations for this are discussed

Examination of the Efficacy of Proximity Warning Devices for Young and Older Drivers

OBJECTIVESThe study was conducted to examine the efficacy of uni- and multi-modal proximity warningdevices for forward object collision and side-object detection for young and older adult drivers.METHODSTwo experiments were conducted, each with 20 young (18 to 30 years of age) and 20 older (61to 80 years of age) healthy and high functioning drivers. In each, participants drove a series ofbrief (~ 4 minute) highway scenarios with temporally unpredictable forward and side collisionevents (i.e., other vehicles). The experiments were conducted in a fixed-base Drive Safetysimulator with a 135-degree wrap-around forward field and a 135-degree rear field. Light crosswindswere included in Experiment 1, while heavier crosswinds were introduced in the secondexperiment. A secondary visual read-out task from an in-vehicle LCD display was also includedin the second experiment.In Experiment 1, potential collision events were signaled 2.2 seconds before impact by visual,auditory, auditory+visual or tactile+visual warnings that were spatially mapped to the location ofthe obstacle (left, right or center). A control condition in which subjects drove without anyproximity warning device was also included in the experiment. Experiment 2 included thecontrol, auditory+visual and visual warnings from Experiment 1.A number of dependent measures were collected, including velocity, lane position, steeringwheel movement, brake and accelerator position. However, we will focus on the response time(as measured by steering wheel deflections or removal of the foot from the accelerator) topotential collision events as well as the number of collisions in different experimental conditions.RESULTSIn both Experiments 1 and 2, the auditory+visual warning device produced the most rapidresponse and also resulted in the fewest collisions. The reduction in response time and collisions,relative to the no-warning control condition was larger in Experiment 2 than in Experiment 1, likely as a result to the more challenging driving scenarios (with the higher and unpredictablewinds and introduction of the secondary task) in this experiment.Older adults responded just as quickly as younger adults to the potential collision events in bothof the experiments. This is a very surprising finding given a voluminous laboratory literature,which suggests that older adults display slower responses than younger adults on almost any taskthat has been examined in the laboratory.In an effort to understand the age-equivalent response times to collision events, we asked youngand older participants from the first experiment to take part in an additional experimental sessionin which they made simple and choice responses to visual and auditory events in a soundattenuated subject booth. Older adults were substantially (~ 35%) slower in each of these simpleand choice tasks performed in the laboratory.Older adults displayed the same performance benefits (in terms of speeded response time andreductions in collisions) from the proximity warning devices, and particularly theauditory+visual device, in both of the experiments as younger adults. However, in Experiment 2,older adults displayed these benefits by neglecting the number read-out secondary task.CONCLUSIONSThere are several important conclusions from the present study. First, proximity warningdevices, and particularly auditory+visual devices, can substantially speed response time andreduce potential collisions in simulated driving. This is an important observation that has thepotential to reduce automobile accidents. Second, both younger and older adults benefit from theproximity warning devices. Such a finding suggests, that at least for individuals with normalvision and hearing, these devices might have substantial utility across a wide variety of drivers.Third, quite to our surprise, older adult drivers responded just as quickly, with and without theproximity warning devices, to potential collision events as younger drivers. Interestingly, ageequivalencein response time to potential collisions was not observed in simple and choiceauditory and visual laboratory response time tasks. Such data tentatively suggests that experienceand expertise in driving may act as a moderator of age-related decline in general slowing.Given the unpredictable nature of the potential collision events in our study, older drivers may becapitalizing on high levels of vigilance and attentional focus on driving relevant tasks to maintaintheir ability to rapidly respond to collision events. This hypothesis is supported, in part, by thedecrements in secondary task performed observed for the older but not for the younger adults inExperiment 2.The results from the present study are encouraging both with respect to the utility of proximitywarning devices as a means to enhance driver safety as well as for their potential application todrivers of different ages and experience levels. However, clearly additional research will beneeded to verify these results in more challenging simulator and on the road driving situations

Differential Effects of Focal and Ambient Visual Processing Demands on Driving Performance

In this study, the differential effects of focal and ambient visual demand on driving were investigated. Subjects participated in a dual-task experiment in which they performed a driving simulation task and a focal or ambient side-task. It was predicted that the focal side-task would cause a significant deterioration in the maintenance of longitudinal control but not lateral control, while there should be no effects of the ambient side-task on driving performance. In general, the results suggest a differentiation in the processing demands of focal and ambient vision

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

Health Literacy and Medication Practices in Senior Housing Residents

Objective: To conduct a descriptive analysis of health literacy, knowledge of prescribed medications, and methods of administering medications in a cohort of senior housing residents.https://scholarworks.uvm.edu/comphp_gallery/1027/thumbnail.jp

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