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<p>These are supplemental materials for </p> <p>Mirman, J.H., Curry, A., and Mirman, D. (2019). Learning-to-drive: A reconceptualization. <em>Transportation Research Part F: Psychology and Behaviour, 62</em>, 316-326. <a href="https://doi.org/10.1016/j.trf.2019.01.010" rel="nofollow">https://doi.org/10.1016/j.trf.2019.01.010</a></p> <p><strong>Abstract</strong> Drivers’ population-level crash rates incrementally decrease following licensure, which has led to the implicit assumption that an individual driver’s crash risk also decreases incre-mentally after licensure as they accrue experience. However, in the aggregate data an incremental decrease in crash rate can reflect both incremental reductions in crash risk within individuals and an incremental increase in the proportion of drivers who have expe-rienced an abrupt decrease in crash risk. Therefore, while it is true to say that the popula-tion of drivers’ crash risk reduces in the months following licensure, it is not necessarily true to say that a driver’s crash risk reduces in the months following licensure; that is, it cannot be assumed that individual-level changes in crash risk mirror the population-level changes in crash rates. In statistics, this is known as an ecological fallacy and in formal logic it is known as the fallacy of division, a type of category error. Using computational cognitive modeling methods we demonstrate that aggregating individual-level abrupt decreases in crash risk (i.e., non-incremental change trajectories) accurately fits population-level crash rate data from over 1 million novice drivers and uniquely accounts for effects of two interventions found to reduce police-reported MVCs. Thus, we demon-strate that: (1) a power-law artifact is readily observable in newly licensed drivers’ aggre-gate crash data, which is not necessarily indicative of individual-level change processes, (2)interventions can alter crash risk trajectories by inducing immediate phase changes in crash risk into a lower risk stratum, or increasing the probability of such a change, and(3) a phase transition model provides a stronger and more parsimonious account of the existing data than an incremental-accrual model.</p>
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