Assessment of Driving Mental Models as a Predictor of Crashes and Moving Violations
MetadataShow full item record
The purpose of the current study was to assess the efficacy of mental models as a predictor of driving outcomes. In contrast to more traditional measures of knowledge, mental models capture the configural property of knowledge, that is, an individual's understanding of the interrelationships that exist among critical concepts within a particular knowledge domain. Given that research has consistently shown the usefulness of mental models for the prediction of performance in a number of settings, it was hypothesized that the development of accurate driving mental models would also play an important role in the prediction of driving outcomes, especially in comparison to traditional measures of driving knowledge—such as the multiple-choice type tests typically required to obtain a driver license. Mental models of 130 college students (52 percent females) between 17 and 21 years-old (M = 18.68, SD = 0.80) were analyzed and compared to a subject matter expert (SME) referent structure using Pathfinder. A statistically significant correlation was found for mental model accuracy and moving violations (r = –.18, p <.05), but not for at-fault crashes. Evidence of incremental validity of mental models over commonly used predictors of moving violations (but not for at-fault crashes) was also found. Exploratory analyses revealed that driving knowledge, general mental ability (GMA), and emotional stability were the best predictors of mental model accuracy. Issues related to the measurement of mental models were extensively addressed. First, statistically significant correlations between GMA and several mental model properties (i.e., accuracy scores, within participant similarity, and within participant correlation) suggest that challenges inherent to the task for eliciting mental models may influence mental model scores which, in turn, may lower mental model reliability estimates. Also, the selection of model components (i.e., terms) and the identification of the "best" reference structure for deriving mental model accuracy scores are undoubtedly critical aspects of mental model-related research. Along with illustrating the decisions made in the context of this particular study, some suggestions for conducting mental model-related research are provided.
motor traffic accidents
Munoz Galvez, Gonzalo Javier (2011). Assessment of Driving Mental Models as a Predictor of Crashes and Moving Violations. Master's thesis, Texas A&M University. Available electronically from