ABSTRACT
This study investigated digital map reading performances of older and younger participants across map concepts. Participants answered questions related to their map reading while driving in a simulator. Mixed ANOVA analyses were completed on the dependent variables: response time, self-reported difficulty, and eye glance behavior. There was a significant difference in response time, with older participants requiring longer time to respond. Although no significant differences were found for eye glance duration towards the map, there were significant differences for the number of eye glances toward the maps with older participants glancing at the maps twice as often as younger participants. Younger participants had significantly longer glance durations towards the driving scene. It is suggested that the higher number of glances reflects the older participants' need to retain the information in working memory. This proves useful in better understanding the cognitive and visual processes of older drivers while reading digital maps.
- AAA & The Institute of Mobility, Activity, and Participation at the University of Florida. 2013. Retrieved May 2015 from http://newsroom.aaa.com/wpcontent/uploads/2013/11/SmartFeaturesFactSheet.pdfGoogle Scholar
- Nazan Aksan, Jeffrey D. Dawson, Jamie L. Emerson, Lixi Yu, Ergun Y. Uc, Steven W. Anderson, and Matthew Rizzo. 2014. Naturalistic distraction and driving safety in older drivers. Human Factors: The Journal of the Human Factors and Ergonomics Society, 55, 4: 841--853.Google ScholarCross Ref
- Kaarin J. Anstey and Joanne Wood. 2011. Chronological age and age-related cognitive deficits are associated with an increase in multiple types of driving errors in late life. Neuropsychology, 25, 5: 613--621.Google ScholarCross Ref
- Carryl L. Baldwin. 2002. Designing in-vehicle technologies for older drivers application of sensory-cognitive interaction theory. Theoretical Issues in Ergonomics Science, 3, 4: 307--329.Google ScholarCross Ref
- Matthieu P. Boisgontier, Isabelle Olivier, Olivier Chenu, and Vincent Nougier. 2012. Presbypropria: the effects of physiological ageing on propioceptive control. AGE, 34, 5: 1179--1194.Google ScholarCross Ref
- Neil Charness and Walter R. Boot. 2009. Aging and information technology use potential and barriers. Current Directions in Psychological Science, 18, 5: 253--258.Google ScholarCross Ref
- Emanuele Coluccia, Andrea Bosco, and Maria A. Brandimonte. 2007. The role of visuo-spatial working memory in map learning: new findings from a map drawing paradigm. Psychological Research, 71, 3: 359--372.Google ScholarCross Ref
- Ragnhild J. Davidse. 2006. Older drivers and ADASwhich systems improve safety-. IATSS Research, 30, 1: 6--20.Google ScholarCross Ref
- Ian J. Deary, Janie Corley, Alan J. Gow, Sarah E. Harris, Lorna M. Houlihan, Riccardo E. Marioni, Lars Penke, Snorri B. Rafnsson, and John M. Starr. 2009. Age-associated cognitive decline. British Medical Bulletin, 92, 1: 135--152.Google ScholarCross Ref
- Green, P., & Williams, M. (1992). Perspective in orientation/navigation display: a human factors test. Vehicle Navigation & Information Systems, 221--226.Google Scholar
- Tina Iachini, Carla Poderico, Gennaro Ruggiero, and Alessandro Iavarone. 2005. Age differences in mental scanning of locomotor maps. Disability and Rehabilitation, 27, 13: 741--752.Google ScholarCross Ref
- Lisa Jenkins, Joel Myerson, Jennifer A. Joerding, and Sandra Hale. 2000. Converging evidence that visuospatial cognition is more age-sensitive than verbal cognition. Psychology and Aging, 15, 1: 157--175.Google ScholarCross Ref
- Donald W. Kline, Theresa J. B. Kline, James L. Fozard, William Kosnik, Frank Schieber, and Robert Sekuler. 1992. Vision, aging, and driving: the problems of older drivers. Journal of Gerontology: Psychological Sciences, 47, 1: 27--34.Google ScholarCross Ref
- Lidia P. Kostyniuk, Fredrick M. Streff, and David W. Eby. 1997. The older driver and navigation assistance systems. The University of Michigan. Ann Arbor: Transportation Research Institute.Google Scholar
- National Highway Traffic Safety Administration. 2014. Traffic Safety Facts 2012 Data. Retrieved May 2015, from National Highway Traffic Safety Administration: http://www.nrd.nhtsa.dot.gov/Pubs/812005.pdfGoogle Scholar
- Cynthia Owsley, Karlene Ball, Michael E. Sloane, Daniel L. Roenker, and John R. Bruni. 1991. Visual/cognitive correlates of vehicle accidents in older drivers. Psychology of Aging, 6, 3: 403--415.Google ScholarCross Ref
- Herbert Pick. 2010. Spatial Orientation and Navigation in Elderly Drivers. University of Minnesota, Center For Transporation Studies. Minnesota: Intelligent Transportation Systems Institute.Google Scholar
- Roger Ratcliff, Anjali Thapar, and Gail McKoon. 2001. The effects of aging on reaction time in a signal detection task. Psychology and Aging, 16, 2: 323--341.Google ScholarCross Ref
- Timothy A. Salthouse, Thomas M. Atkinson, and Diane E. Berish. 2003. Executive functioning as a potential mediator of age-related cognitive decline in normal adults. Journal of Experimental Psychology, 132, 4: 566--594.Google ScholarCross Ref
- Martina Ziefle, Preethy Pappachan, Eva-Maria Jakobs, and Henning Wallentowitz. 2008. Visual and auditory interfaces of advanced driver assistant systems for older drivers. Computer Science, 5105: 62--65. Google ScholarDigital Library
Index Terms
- Older Users and In-Vehicle Navigation Map Design Elements
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