Location

Big Sky, Montana

Date

25-6-2009

Session

Session 8 – Lectures Elderly Drivers

Abstract

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.

Rights

Copyright © 2009 the author(s)

DC Citation

Proceedings of the Fifth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, June 22-25, 2009, Big Sky, Montana. Iowa City, IA: Public Policy Center, University of Iowa, 2009: 506-513.

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Jun 25th, 12:00 AM

Attention Function Structure of Older and Younger Adult Drivers

Big Sky, Montana

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.