Assessments of risky driving: A Go/No-Go simulator driving task to evaluate risky decision-making and associated behavioral patterns

Ba, Yutao; Zhang, Weigang; Salvendy, Gavriel; Cheng, Andy S. K.; Ventsislavova, Petya

doi:10.1016/j.apergo.2015.07.020

Cited by 30 publications

(11 citation statements)

References 41 publications

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“…Inspired by Kahneman’s dual process theory, that irrational decision making increases when cognitive resources become depleted ( Kahneman, 2003 ; Kahneman and Frederick, 2007 ; Kahneman, 2011 ), some have tested the influence of executive control on risky decision making by administering the n-back task, a popular working memory task, in parallel with various risky decision-making tasks ( Whitney, Rinehart and Hinson, 2008 ; Starcke et al, 2011 ; Farrell et al, 2012 ; Pabst et al, 2013 ; Gathmann et al, 2014a , b ). Likewise, many have examined inhibitory processes and risky decision making by employing the Go No-Go ( Verdejo-García et al, 2007 ; Yeomans and Brace, 2015 ; Ba et al, 2016 ; Welsh et al, 2017 ). However, to date few have examined the link between set-switching and risky decision making ( Verdejo-García et al, 2007 ; Fröber and Dreisbach, 2016 ); therefore, we proposed to investigate this link by using brain stimulation of the PFC.…”

Section: Introductionmentioning

confidence: 99%

Transcranial Alternating Current Stimulation Modulates Risky Decision Making in a Frequency-Controlled Experiment

Yaple

Martinez-Saito

Awasthi

et al. 2017

eNeuro

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In this study, we investigated the effect of transcranial alternating current stimulation (tACS) on voluntary risky decision making and executive control in humans. Stimulation was delivered online at 5 Hz (θ), 10 Hz (α), 20 Hz (β), and 40 Hz (γ) on the left and right frontal area while participants performed a modified risky decision-making task. This task allowed participants to voluntarily select between risky and certain decisions associated with potential gains or losses, while simultaneously measuring the cognitive control component (voluntary switching) of decision making. The purpose of this experimental design was to test whether voluntary risky decision making and executive control can be modulated with tACS in a frequency-specific manner. Our results revealed a robust effect of a 20-Hz stimulation over the left prefrontal area that significantly increased voluntary risky decision making, which may suggest a possible link between risky decision making and reward processing, underlined by β-oscillatory activity.

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Section: Introductionmentioning

confidence: 99%

Transcranial Alternating Current Stimulation Modulates Risky Decision Making in a Frequency-Controlled Experiment

Yaple

Martinez-Saito

Awasthi

et al. 2017

eNeuro

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“…A previous longitudinal neuroimaging study reported that less thinning of the orbitofrontal cortex during adolescence is associated with risky driving behavior in young people (Vijayakumar et al, 2019). Given that a previous driving simulator study reported that the number of violations and accidents was positively correlated with the tendency to make risky decisions in dilemma situations (Ba et al, 2016), structural alterations of this cortical region may have strong effects on unsafe driving behaviors.…”

Section: Figure 3 |mentioning

confidence: 95%

Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

Yamamoto

Yamagata

Hirano

et al. 2020

Front. Aging Neurosci.

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In developed countries, the number of traffic accidents caused by older drivers is increasing. Approximately half of the older drivers who cause fatal accidents are cognitively normal. Thus, it is important to identify older drivers who are cognitively normal but at high risk of causing fatal traffic accidents. However, no standardized method for assessing the driving ability of older drivers has been established. We aimed to establish an objective assessment of driving ability and to clarify the neural basis of unsafe driving in healthy older people. We enrolled 32 healthy older individuals aged over 65 years and classified unsafe drivers using an on-road driving test. We then utilized a machine learning approach to distinguish unsafe drivers from safe drivers based on clinical features and gray matter volume data. Twenty-one participants were classified as safe drivers and 11 participants as unsafe drivers. A linear support vector machine classifier successfully distinguished unsafe drivers from safe drivers with 87.5% accuracy (sensitivity of 63.6% and specificity of 100%). Five parameters (age and gray matter volume in four cortical regions, including the left superior part of the precentral sulcus, the left sulcus intermedius primus [of Jensen], the right orbital part of the inferior frontal gyrus, and the right superior frontal sulcus), were consistently selected as features for the final classification model. Our findings indicate that the cortical regions implicated in voluntary orienting of attention, decision making, and working memory may constitute the essential neural basis of driving behavior.

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“…Finally, a Go No-Go driving simulator task was also used to evaluate drivers' RDM. 19 with a decrease in collisions. Moreover, it has been found that hazard scores correlate with the execution of abrupt braking during real driving.…”

Section: Driving: a Question Of Skill And Assumed Riskmentioning

confidence: 97%

Risky Decision-Making and Hazard Prediction are Negatively Related and Could Be Assessed Independently Using Driving Footage

Castro

Ventsislavova

García-Fernández

2021

PRBM

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Introduction: Traffic collisions are a principal cause of death in Europe, disproportionately affecting young drivers. Driving safety depends not only on our ability to anticipate and respond to dangers on the road but also on the level of risk we are willing to engage within our own driving behaviour. Methods: Hazard prediction (HPr) and risky decision-making (RDM) tests were given to three groups of young Spaniards (169 participants): 54 non-drivers (M=20), 65 novice (M=21) and, 50 experienced drivers (M=26 years old). Both tests presented participants with video clips of driving recorded from the driver's perspective. The HPr test contained hazardous situations caused by the actions of another road user (eg, a pedestrian crossing the road). Each HPr clip was occluded as a hazard began to unfold and participants were asked to predict "what happens next?" They selected their answer from four on-screen options. The RDM test used clips where any imminent danger would be provoked by the film-car driver's risky behaviour (eg, overtaking illegally). Participants were asked to report the probability of following certain types of risky behaviour (eg, "Would you go forward with the lights on amber?" or "Would you overtake the cyclist/lorry/bus at this point?"). In addition, the effect of the locality of the driving scenarios was manipulated: they could take place in the participant's native country (Spain) or in a different country (UK). Results: Non-drivers and novice drivers were less able to predict upcoming hazards and more likely to make risky decisions. Driving scenarios from another country (UK) provoked riskier decisions than those from the participants' home country (Spain). Conclusion: Improvement in HPr skills among novice or new drivers poses a huge challenge as far as driver training is concerned, though it is only part of the solution. Young inexperienced drivers' willingness to engage in risky behaviour also needs to be tackled. Our results suggest that such RDM can be assessed in a similar way to HPr skill, using a naturalistic approach, which raises the possibility of assessing and training drivers on a wider range of safety-related behaviours.

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Assessments of risky driving: A Go/No-Go simulator driving task to evaluate risky decision-making and associated behavioral patterns

Cited by 30 publications

References 41 publications

Transcranial Alternating Current Stimulation Modulates Risky Decision Making in a Frequency-Controlled Experiment

Transcranial Alternating Current Stimulation Modulates Risky Decision Making in a Frequency-Controlled Experiment

Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People

Risky Decision-Making and Hazard Prediction are Negatively Related and Could Be Assessed Independently Using Driving Footage

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