September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
3D-MOT predicts driving skills in older drivers
Author Affiliations & Notes
  • Jesse Michaels
    Visual Psychophysics and Perception Laboratory, School of Optometry, University of Montreal, Montreal, Quebec, Canada
  • Donald Watanabe
    Visual Psychophysics and Perception Laboratory, School of Optometry, University of Montreal, Montreal, Quebec, Canada
  • Pierro Hirsch
    Virage Simulation, Montreal, Quebec, Canada
  • Francois Bellavance
    Transport Safety Laboratory, HEC Montreal, Montreal, Quebec, Canada
  • Jocelyn Faubert
    Visual Psychophysics and Perception Laboratory, School of Optometry, University of Montreal, Montreal, Quebec, Canada
  • Footnotes
    Commercial Relationships   Jesse Michaels, None; Donald Watanabe, None; Pierro Hirsch, Virage Simulation (E); Francois Bellavance, None; Jocelyn Faubert, CogniSens Inc. (E), CogniSens Inc. (I)
  • Footnotes
    Support  SAAQ-FRQSC support, continued work funded by NSERC-Essilor Industrial Research Chair and CogniSens Inc.
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 1505. doi:
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    • Get Citation

      Jesse Michaels, Donald Watanabe, Pierro Hirsch, Francois Bellavance, Jocelyn Faubert; 3D-MOT predicts driving skills in older drivers. Invest. Ophthalmol. Vis. Sci. 2016;57(12):1505.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose : There are a number of perceptual and cognitive ability changes that accompany the aging process (Faubert, 2002; Salthouse et al., 1995). Driving is both a highly visual and complex task that places high demands on perceptual, cognitive, and motor processes (Owsley, 2010). Further, the level of ability for processing a “multiple object tracking” (MOT) task has been linked with performance outcomes during dynamic scene processing such as in sports (Faubert & Sidebottom, 2012; Faubert, 2013; Mangine et al., 2015) but has yet to clearly associated with driving in older adults. The purpose of the present study was to assess the relationship between initial measures of a 3D-MOT task (NeuroTrackerTM) and measures for static and motion grating stimuli defined by luminance or texture contrast with driving metrics obtained with a car simulator in older adults.

Methods : We tested 115 individuals between 18 to 86 years of age (51 subjects above 70 years of age). Each subject came for 2 visits. In the first visit the participants were given an optometric exam, which included acuities (ETDRS), visual fields (HVF) and stereoscopy (Frisby, Randot). They were assessed using questionnaires for cognitive abilities (Mini-Mental State Examination), for cybersickness (SAS), and driving behaviour. They were also tested for static (orientation thresholds) and moving (left-right) grating stimuli defined either by luminance (first-order) or texture (second-order) contrast. Finally they were given a brief adaptation to the car simulator while driving in urban and AutoRoute scenarios. In the second visit the subjects were tested on 3 different car scenarios representing rural, urban and AutoRoute driving environments each of which had a number of critical events used to challenge the driver.

Results : The perceptual-cognitive results were correlated with a series of metrics obtained from the car simulator data, most notably, number of crashes, number of near crashes, mean speed and speed of vehicle during gas pedal release when confronted with simulated events. The data show that the NeuroTracker measures were significantly correlated with all these metrics. Second order motion thresholds were correlated with mean speed and brake release speed only. First order measures were not correlated with the driving outcomes.

Conclusions : The NeuroTracker measures (3D-MOT) were very good predictors of driving outcomes in our subjects.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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