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    The impact of cognitive load on a lane change task (LCT) among male autistic individuals: A driving simulator study

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    Date
    2024-10-31
    Author
    Wondwesen Girma, Mamo
    Alhajyaseen, Wael K.M.
    Brijs, Kris
    Dirix, Hélène
    Vanroelen, Giovanni
    Hussain, Qinaat
    Brijs, Tom
    Ross, Veerle
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    Abstract
    This study investigated the impact of cognitive load on driving among autistic individuals with the use of an adapted, driving simulator-based, Lane Change Task (LCT). A secondary task was used to induce increasing verbal WM load. A total of 51 male participants, 17 autistic and 34 non-autistic individuals participated in the study. Participants drove the simulator-based LCT without (baseline) and with a three-level auditory-verbal response N-back task (i.e., 0-back,1-back, and 2-back) developed to tax working memory capacity. The included driving parameters were: mean deviation in the lane change path (MDEV), percentage of correct lane changes (PCL) in response to a lane change sign, and lane change initiation (LCI). The percentage of error rate (PER) was included to measure participants’ performance on the secondary task. Dual-task performance of both groups deteriorated with increasing cognitive load, but this effect was more pronounced in the autistic group. Specifically, the performance of both group on MDEV, PCL, and PER suffered from the increasing cognitive load. Nevertheless, neither PCL nor LCI differ between autistic and non-autistic participants. Notably, LCI also deteriorated with increasing cognitive load for non-autistic participants, but not for autistic participants. Similar to previous research, it is suggested that distracted driving should be eliminated as much as possible before occurring in the first place. Specific suggestions for eliminating distraction in autistic drivers are provided.
    URI
    https://www.sciencedirect.com/science/article/pii/S1369847824001992
    DOI/handle
    http://dx.doi.org/10.1016/j.trf.2024.07.030
    http://hdl.handle.net/10576/58413
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    • Civil and Environmental Engineering [‎862‎ items ]

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