Risk-taking on the road and in the mind: behavioural and neural patterns of decision making between risky and safe drivers. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Ba, Yutao; Zhang, Wei; Peng, QiJia; Salvendy, Gavriel; Crundall, David
• Is Part Of: Ergonomics. Volume 59:Issue 1(2016)
• Page Start: 27
• Page End: 38
• Publication Date: 2016-01-02
• Subjects: transportation safety -- driving -- event related potentials -- decision making -- individual differences; Human engineering -- Periodicals; Cybernetics -- Periodicals; Industrial management -- Periodicals; Ergonomie -- Périodiques; Cybernétique -- Périodiques; Gestion d'entreprise -- Périodiques; 620.8205
• DOI: 10.1080/00140139.2015.1056236
• Languages: English
• ISSNs: 0014-0139

Abstract

Abstract : Drivers' risk-taking is a key issue of road safety. This study explored individual differences in drivers' decision-making, linking external behaviours to internal neural activity, to reveal the cognitive mechanisms of risky driving. Twenty-four male drivers were split into two groups (risky vs. safe drivers) via the Drivier Behaviour Questionnaire-violation. The risky drivers demonstrated higher preference for the risky choices in the paradigms of Iowa Gambling Task and Balloon Analogue Risk Task. More importantly, the risky drivers showed lower amplitudes of feedback-related negativity (FRN) and loss-minus-gain FRN in both paradigms, which indicated their neural processing of error-detection. A significant difference of P300 amplitudes was also reported between groups, which indicated their neural processing of reward-evaluation and were modified by specific paradigm and feedback. These results suggested that the neural basis of risky driving was the decision patterns less revised by losses and more motivated by rewards. Abstract : Practitioner Summary: Risk-taking on the road is largely determined by inherent cognitive mechanisms, which can be indicated by the behavioural and neural patterns of decision-making. In this regard, it is feasible to quantize drivers' riskiness in the cognitive stage before actual risky driving or accidents, and intervene accordingly.