## 2 Materials and Methods (Study 1)
### Data Analysis Process  
Statistical analyses were conducted using R, a programming language for statistical computing [44]. Data analysis was organized into two phases.  
In the first phase, the goal was to identify whether driving performance differed across the FOD-A combinations and, if so, on which performance variables they differed. For this reason, a one-way multivariate analysis of variance (MANOVA) was conducted [45]. The one-way MANOVA, while controlling for within-subjects variability [46; 47; 48], was performed using the FOD-A combinations as the independent variable with four levels and the 11 measures of driving performance as the dependent variables. If the MANOVA showed that differences on simulator performance variables existed, then a univariate one-way analysis of variance (ANOVA) was conducted on each of the 11 dependent variables to determine where differences existed among the four different combinations, followed by [PERSON]'s HSD for the pairwise comparison [49; 50].  
In the second phase, there were two goals: to determine which variables contributed the most to the classification of the FOD-A combinations and to rank order the FOD-A combinations based on the significant variables. To achieve these goals, a linear discriminant analysis (LDA) was conducted. Linear discriminant analysis, also known as discriminant function analysis, is a statistical method that determines an optimal linear combination of features that characterize or separate two or more groups [51; 52]. This optimal linear combination (or discriminant function) can also be used for classification. In LDA, the number of discriminant functions is equal to the number of groups minus one. Because there were four FOD-A combinations, there could be up to three discriminant functions (note, only two discriminant functions were reported in our results below because the third function contributed less than 1% to the discriminability of the FOD-A combinations). The LDA results in an estimated weight, or coefficient, for each dependent variable which represents  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Functional Object Detection—Advanced Tasks**} & \multicolumn{4}{c}{**FOD—Advanced Combinations**} \\ \cline{2-5}  & **1** & **2** & **3** & **4** \\ \hline Respond to the lead vehicle’s brake lights? & Yes & Yes & Yes & Yes \\ \hline Respond to the target Es? & Yes & Yes & Yes & Yes \\ \hline Control vehicle position using the steering wheel? & No & Yes & Yes & Yes \\ \hline Control speed using the gas pedal? & No & Yes & Yes & Yes \\ \hline Respond to the distractor (\& backwards) Es? & No & No & Yes & Yes \\ \hline Complete the mirror task after the brake task? & No & No & No & Yes \\ \hline How do target Es appear on screen? & Pop & Pop & Fade & Fade \\ \hline \end{tabular}
\end{table}
Table 1: Combination of tasks used in this study for each FOD—Advanced combination.  
the contribution of that variable to the discriminant function. A particular discriminant function results in an equation where each variable was multiplied by a weight. Thus, a discriminant function can be thought of as a new variable such that each participant has a score on the discriminant function. This score is simply a weighted combination of the variables. When interpreting a discriminant function, it is typical to inspect the standardized weights which represent the relative importance of a variable's contribution to the discriminant function. The first discriminant function revealed the influence of the simulator performance variables and their ability to distinguish among the combinations. The second discriminant function was used in conjunction with the first discriminant function to rank the FOD-A combinations examined in this study.  
The data analysis was performed in this order: MANOVA, subsequent ANOVAs for significant variables, then the linear discriminant analysis. Although the analyses were conducted in this sequence, the results in the discussion are presented in a different order for clarification.