|M.Sc Student||Eyal Hershkovitz|
|Subject||Cognitive Modeling Analysis of Decision Making Processes and|
Personality Traits in Dangerous Drivers
|Department||Department of Industrial Engineering and Management||Supervisor||Full Professor Yechiam Eldad|
|Full Thesis text|
The last several years have been marked by a growing concern regarding the harsh consequences of driving accidents as well as an increased interest in traffic safety (Harré, 2000; West et al., 1993). At-risk driving behavior has been found to be a central component in the predicament of road traffic injuries. Ample scholarly attention has been devoted to understanding risky behavior in general and particularly the behavior of risky drivers. However, while some attribute at-risk driving to an innate cognitive style, others argue that certain individuals simply possess a personality makeup that renders them more prone to risky behavior.
In a laboratory setting, the present study compared both the decision making style and the personality attributes of traffic offenders and non-offenders. Forty-nine traffic offender (31 of which moderate offenders and 18 severe offenders) who attended the Israeli Ministry of Transportation Penalty course were recruited on site and compared with a control group of 35 non-offenders.
The results confirmed that both the decision making style and the personality approaches independently provide explanatory capabilities to at-risk driving behavior. Specifically, the results indicate that traffic offenders tended to make riskier choices while performing the Iowa Gambling Task (IGT), a popular decision task employed for assessing cognitive impulsivity. Moreover, analysis using a formal cognitive model, the Expectancy Valance (EV) model, suggests that differences among drivers might result from offenders’ high weighting of gains as compared to losses. The examination of personality, for which I utilized the Big Five personality questionnaire, showed that traffic offenders are more extroverted than their counterparts. It thus appears that while differences in personality traits play a roll in distinguishing at-risk drivers, performance in the IGT coupled with the EV model can also be useful for studying individual differences in driving behavior, and for identifying the sources of these differences.