The impact of N-back-induced mental workload and time budget on takeover performance

Mental Workload (MWL) refers to the specification of information processing capacity used for maintaining task performance. Some studies find no effects of high MWL on the timing and quality of takeovers, whilst others have found increases in crash risk and delayed response times. The effect of time budget - the time between event onset and an impending crash - is much clearer; drivers react faster when time budgets are smaller. However, no study has investigated whether the effects of a pure MWL interact with the effects of time budget during critical takeovers from a hands-off Level 2 (L2) driving system. A Bayesian multilevel modelling approach was used to quantify the direction, size, and uncertainty of the effects that MWL and time budget have on driver performance. Drivers (N = 37) used a hands-off L2 driving system: once while completing a pure MWL task (2-back) and another while monitoring the road. Rear-end conflicts were generated via lead vehicles decelerating with short (TTC = 3 s) or long (TTC = 5 s) time budgets. 2-back-induced MWL had no consistent or substantial impact on the timing or quality of takeovers. Conversely, drivers were faster to respond but more erratic in their post-takeover lateral control following events with smaller time budgets. We discuss the reasons for the absence of effects from the 2-back-induced MWL on takeover performance. One suggestion is that rear-end scenarios elicit automatised behaviours that do not rely upon cognitive control and thus remained unaffected by MWL. Conversely, scenarios that require cognitive control (e.g., lane change manoeuvres or hazard perception tasks) may be more susceptible to the detrimental effects of MWL during transitions of control.

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Publication Details

Journal
Accident Analysis & Prevention
Published
2025-11-25
DOI
https://doi.org/10.1016/j.aap.2025.108327
Citations
1
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
2.44
Controls
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article

The impact of N-back-induced mental workload and time budget on takeover performance

Albert Solernou, Anthony Horrobin, Natasha Merat, Yee Thung Lee et al.
1 citations
Accident Analysis & Prevention
Human-Automation Interaction and Safety
2.44
article

The impact of N-back-induced mental workload and time budget on takeover performance

Albert Solernou, Anthony Horrobin, Natasha Merat, Yee Thung Lee, Yee Mun Lee, Ali Arabian, Courtney M Goodridge, Audrey Bruneau
article en
1 citations

Abstract

Mental Workload (MWL) refers to the specification of information processing capacity used for maintaining task performance. Some studies find no effects of high MWL on the timing and quality of takeovers, whilst others have found increases in crash risk and delayed response times. The effect of time budget - the time between event onset and an impending crash - is much clearer; drivers react faster when time budgets are smaller. However, no study has investigated whether the effects of a pure MWL interact with the effects of time budget during critical takeovers from a hands-off Level 2 (L2) driving system. A Bayesian multilevel modelling approach was used to quantify the direction, size, and uncertainty of the effects that MWL and time budget have on driver performance. Drivers (N = 37) used a hands-off L2 driving system: once while completing a pure MWL task (2-back) and another while monitoring the road. Rear-end conflicts were generated via lead vehicles decelerating with short (TTC = 3 s) or long (TTC = 5 s) time budgets. 2-back-induced MWL had no consistent or substantial impact on the timing or quality of takeovers. Conversely, drivers were faster to respond but more erratic in their post-takeover lateral control following events with smaller time budgets. We discuss the reasons for the absence of effects from the 2-back-induced MWL on takeover performance. One suggestion is that rear-end scenarios elicit automatised behaviours that do not rely upon cognitive control and thus remained unaffected by MWL. Conversely, scenarios that require cognitive control (e.g., lane change manoeuvres or hazard perception tasks) may be more susceptible to the detrimental effects of MWL during transitions of control.

Accident Analysis & PreventionVol. 225
University of Leeds (GB), Toyota Motor Corporation (Belgium) (BE)
Openalex Percentile: Top 8%
Human-Automation Interaction and Safety
2.44
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