title: Using the Attention Network Test to predict driving test scores.
abstract: Driving is a complex multi-factorial task that taps underlying mechanisms of cognition and attention. Not surprisingly, therefore, many tests of cognition and attention are significantly associated with driving outcomes. In this article, we introduce driving researchers and clinicians with an interest in driving to the Attention Network Test (ANT), which to our knowledge has not previously been used in driving research. It is a recently developed test that is based on a neural network model of the human attention system. It combines elements of Posner's cuing paradigm [Posner, M.I., 1980. Orienting of attention. Quarterly Journal of Experimental Psychology 32, 3-25.] with the Eriksen & Eriksen flanker task [Eriksen, B.A., Eriksen, C.W., 1974. Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics 16, 143-149.], and provides measures of three distinct functions of attention: alerting, orienting, and executive function. Our results demonstrate that the ANT has very good concurrent validity with the Useful Field of View (UFOV), and that it is comparable to UFOV in its ability to predict road test scores for a simulated drive. These findings suggest that further investigation of the usefulness of the ANT as a tool for driving researchers and clinicians is merited.
authors.affiliation: Northern Ontario School of Medicine, Lakehead University, 955 Oliver Road, Thunder Bay, Ontario, Canada. bweaver@lakeheadu.ca
authors.forename: Bruce
authors.initials: B
authors.lastname: Weaver
authors.forename: Michel
authors.initials: M
authors.lastname: Bédard
authors.forename: Jim
authors.initials: J
authors.lastname: McAuliffe
authors.forename: Marie
authors.initials: M
authors.lastname: Parkkari
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001288:Attention / Q000502:physiology*; D001334:Automobile Driving / Q000592:standards / Q000706:statistics & numerical data*; D003071:Cognition; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D009415:Nerve Net; D009483:Neuropsychological Tests; D009864:Ontario; D055815:Young Adult