# Recognition of Customers' Impulsivity from Behavioral Patterns in Virtual Reality
## 3 Results
### Recognition of Impulsivity
#### 3.2.1 Results for Accuracy  
The results of machine learning for impulsivity and its subscales are reported in Table 3. Each scale has two columns consisting of the average of the accuracies and [PERSON]'s kappa coefficient, with their standard deviations in parentheses. The results are categorized based on each signal, and each signal is divided based on the tasks. Besides, for HBT data, the results of combinations of the tasks are reported in Table 4. In that table, to create the second row, the features of the two planned tasks are put together. In the same way, for the third row, all the tasks are combined. Moreover, the colors in these tables are chosen according to the kappa. Green represents the most accurate result, and red represents the least accurate result. Correspondingly, the higher the kappa, the closer the color is to green. To recognize impulsivity, kappa quantities in Table 3 indicate that the best combination was the POS & INT signal and the exploration task with a kappa of 0.64. Additionally, the navigational pattern in this task could show a distinction between the different levels of impulsivity. Moreover, the exploration task could elicit impulsivity and provide a more precise classification overall. For its part, the ET signal did not provide an acceptable level of discrimination. Based on Table 4, combining the signals in HBT improved the kappa by more than 10%, but combining all the tasks did not improve kappa by a considerable amount (less than 3%). In addition, as Table 3 shows, the highest kappa achieved in the first and second planned tasks separately was 0.36, but combining the second and third tasks and using HBT data yielded a kappa of 0.56. Considering the trait of urgency, Table 2 shows that the best signal was POS & INT in the second planned task with kappa equal to 0.44. According to Table 3, when the second planned task was performed, urgency could be predicted by any of the three signals. However, in the exploration task, none of the signals recognized this trait. Table 4 suggests that to recognize this trait, combining the signals and the tasks can improve the results by a considerable percentage (more than 18%). In the case of premeditation, the kappa quantities in Table 3 show that ET and NAV signals failed to create any distinction in the levels of that trait. However, the POS & INT signal provided more acceptable results in the exploration task with kappa equal to 0.39. Based on Table 4, combining the signals and the tasks led to considerable improvement in kappa, such that premeditation could be predicted in almost all the tasks. Hence, to predict premeditation, Table 4 suggests considering all the signals together; otherwise, the results will be poor, according to Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Dimension**}} & \multirow{2}{*}{**Balance**} & \multicolumn{2}{c}{**Centers (Min, Max)**} & \multirow{2}{*}{**Centers (Min, Max)**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**Cronbach’s Alpha**} \\  & & & & **Low Group** & & **of High Group** & \\ \hline Main scale & Impulsivity & 24–33 & 2.31 (1.50–2.62) & 3.20 (2.75–4.38) & \(<\)10\({}^{-5}\) & 0.72 \\ \hline \multirow{4}{*}{Subscales} & Urgency 1 & 23–34 & 1.65 (1.00–2.00) & 3.07 (2.50–5.00) & \(<\)10\({}^{-5}\) & 0.73 \\  & Premeditation 1 & 18–39 & 2.67 (2.0–3.00) & 4.04 (3.50–5.00) & \(<\)10\({}^{-5}\) & 0.80 \\  & Perseverance 1 & 19–38 & 2.05 (1.00–2.50) & 3.76 (3.00–5.00) & \(<\)10\({}^{-5}\) & 0.64 \\  & Sensation-seeking 1 & 27–30 & 3.00 (2.00–3.50) & 4.15 (4.00–5.00) & \(<\)10\({}^{-5}\) & 0.58 \\ \hline \hline \end{tabular}  
* Subscales of the 1-8 questionnaire.  
\end{table} 
Table 2: Extracting the labels based on the questionnaires. For the trait of perseverance, Table 3 indicates that it could not be recognized by ET and NAV signals, and the best result belonged to the POS & INT signal in the second planned task, which was relatively weak (with a kappa equal to 0.38).