# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
## 6 Discussion  
### Results Discussion  
The discussion examines the relationships between personality, cognitive load measures, and physiological data. To the best of our knowledge, this is the first research that tries to examine such results and interpret them. The examination focuses on correlations with at least medium correlation strength (\(\pm\)0.3 as the threshold). Table 3 shows significant correlations between personality traits and physical demand as measured by TLX for the CogLoad dataset. Emotionality and its three facet-level traits--dependence, fearfulness, and anxiety--all significantly positively correlate with subjective physical demand. Since emotionality describes a response to stressful and demanding situations as well as physical danger, the positive correlation is sensible, meaning people that rank high in emotionality also find tasks physically more demanding, and vice versa. Table 4 shows significant correlations between the TLX scores and objective cognitive load measures for the CogLoad dataset. The correlations show people that spend more time on tasks find them more demanding, put in more effort, get more frustrated, but also feel they performed better the more time they spent on them. The negative correlation between the correct answers and perceived performance, however, is unusual. It reports the more people felt they did well, the worse they actually performed. Whether this is due to chance or a measurement problem is unclear, but should be noted as something to be aware of. Deeper psychological profile construction and comparison could yield possible answers for this correlation. It could be people that score low on humility overwhelmingly report higher performance scores, but are very susceptible to cognitive load. That many TLX scores significantly correlate with the task difficulty level is also expected--this mostly confirms the difficulty levels are reasonably set as they are. Table 5 shows significant correlations between personality traits and objective cognitive load measures for the Snake dataset. People higher in emotionality have higher heart rates during solving tasks (and vice versa); people higher in agreeableness have higher temperature during solving tasks, \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & **Model** & **All Features** & \multicolumn{4}{c}{**Selected Features**} \\ \hline  & & **Raw** & **Norm.** & **Stand.** & **Raw** & **Norm.** & **Stand.** \\ \hline  & Majority & 50.0 & 50.0 & 50.0 & 50.0 & 50.0 & 50.0 \\  & Random Forest & 62.4 & 64.9 & 66.8 & 62.9 & 64.5 & 67.9 \\  & AdaBoost & 60.4 & 64.3 & 65.6 & 61.8 & 61.7 & 67.3 \\  & KNN & 51.7 & 59.3 & 63.6 & 58.2 & 59.8 & 64.0 \\  & Naive Bayes & 49.0 & 63.3 & 58.5 & 51.3 & 60.8 & 57.9 \\
**CogLoad** & Decision Tree & 58.6 & 61.8 & 63.9 & 59.6 & 60.4 & 62.0 \\  & Log. Reg. & 60.3 & 62.5 & 64.0 & 61.1 & 63.7 & 65.7 \\  & **Bagging** & 63.9 & 65.0 & 67.4 & 64.7 & 65.2 & **68.2** \\  & XGB & 61.6 & 63.1 & 65.5 & 62.2 & 61.9 & 66.4 \\  & MTL & 63.3 & 64.1 & 63.4 & 63.6 & 64.0 & 65.2 \\  & MLP & 63.7 & 62.2 & 62.8 & 63.9 & 64.3 & 63.1 \\ \hline  & Majority & 51.0 & 51.0 & 51.0 & 51.0 & 51.0 & 51.0 \\  & Random Forest & 60.0 & 67.7 & 68.3 & 61.4 & 66.9 & 70.0 \\  & AdaBoost & 57.7 & 66.0 & 66.6 & 54.6 & 69.4 & 70.3 \\  & KNN & 52.3 & 70.6 & 68.0 & 57.1 & 70.6 & 67.7 \\  & Naive Bayes & 51.4 & 67.1 & 68.6 & 52.9 & 66.0 & 68.6 \\
**Snake** & Decision Tree & 55.4 & 66.0 & 64.6 & 56.0 & 68.3 & 71.4 \\  & Log. Reg. & 56.0 & 67.7 & 68.6 & 56.9 & 66.3 & 70.6 \\  & Bagging & 60.0 & 68.0 & 67.7 & 60.9 & 73.1 & 69.4 \\
**XGB** & 59.1 & 79.1 & 82.0 & 58.0 & 78.3 & **82.3** \\  & MTL & 69.5 & 58.6 & 65.7 & 71.4 & 67.1 & 70.0 \\  & MLP & 60.0 & 61.0 & 63.8 & 62.4 & 62.9 & 65.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Machine learning evaluation results with binary classification accuracy. which is the opposite of expected.