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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14526

Title: Investigating the influence of working memory capacity on driving behavior when combined with cognitive load: an LCT study of young novice drivers
Authors: ROSS, Veerle
WANG, Weixin
WETS, Geert
Issue Date: 2012
Citation: 25th International Co-operation on Theories and Concepts in Traffic Safety (ICTCT) Workshop, Diepenbeek, Belgium, 8-9 November 2012
Abstract: Distracted driving received increasing attention in the literature due to potential adverse safety outcomes. Especially the use of new in-vehicle technologies created situations in which driving is often combined with other tasks. However, operating in-vehicle technology induces working memory load (WM load) and therefore the working memory capacity (WM capacity) of the driver is not only devoted to the primary task of driving. WM capacity consists of different subtypes (visuospatial and verbal). This study investigated if, and how, these types relate to the influence of WM load on driving performance, as measured by a lane changing task (LCT). Young novice drivers (n= 51, age= 17-25), with minimum 20 hours of practice and no more than two years of driving experience, participated in the experiment. Each participant completed two WM capacity tasks, tapping into either visuospatial or verbal WM capacity. The LCT was performed under baseline conditions and in combination with three levels of increasing WM load, induced by an auditory-verbal response N-back. Dependent measures of interest were mean deviation in the lane change path (MDEV), percentage of correct lane changes (PCL), and lane change initiation (LCI). Results showed that with increasing distraction performance on each measure deteriorated. Furthermore, higher WM capacity was related to better LCT performance, but this relation differed per WM capacity measure. More important, for PCL, young novice drivers with high verbal WM capacities were less influenced by distraction. Discarding distraction, in combination with WM capacity training, is proposed as the best solution for minimizing crash risks.
URI: http://hdl.handle.net/1942/14526
Category: C2
Type: Conference Material
Appears in Collections: Research publications

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