## 4 Results and Discussion
### Shap Analysis across 49 Features
#### 4.2.4 Results for Quadrant 3  
Figure 8 identifies 'Max Micro-Saccade Direction' as the most impactful feature on the model's predictions for Quadrant 3, which is characterized by high arousal and negative valence, associated with emotions such as anger, fear, stress, and disgust. The figure reveals that higher values of this feature negatively impact the model's predictions, suggesting that larger angular movements in micro-saccades are indicative of these intense emotional states. This pattern may reflect a behavioral response where individuals experiencing strong negative emotions such as disgust, fear, or stress exhibit rapid, large-angle micro-saccades as a part of their visual scanning or threat detection processes.  
Figure 8: Summary Plot of the First 10 Features for Quadrant 3. (For a complete view of all 49 features, visit: [[https://doi.org/10.6084/m9.figshare.26130589](https://doi.org/10.6084/m9.figshare.26130589)]([https://doi.org/10.6084/m9.figshare.26130589](https://doi.org/10.6084/m9.figshare.26130589)) (accessed on 10 July 2024)).  
Figure 7: Variations in F1-Score Across Different Feature Numbers for Quadrant 2.  
In Quadrant 3, characterized by high arousal and negative valence associated with emotions like anger, fear, and disgust, specific blinking and fixation features significantly influence the model's predictions. It is observed that higher values of 'Mean Blink Duration', 'SD Blink Duration', 'Max Blink Duration', and 'Number of Blink' positively impact the model's predictions. This pattern indicates that individuals tend to blink more frequently and for longer durations when experiencing intense negative emotions, likely as a physiological response to stress or discomfort.  
Additionally, shorter 'First Fixation Duration' has a positive effect on the model's accuracy in Quadrant 3. This suggests that when confronted with disturbing or frightening stimuli, individuals are likely to make brief initial fixations, possibly as a way to avoid distressing visual content.  
However, the influence of the remaining features on the model's predictions shows variability, affecting outcomes both positively and negatively. This complexity underscores the challenge of making generalizations about the relationship between certain eye tracking metrics and specific emotional states in this quadrant. The data indicate that while some eye movement patterns are consistently linked with high-arousal negative emotions, others do not show a clear trend and may depend on additional contextual or individual factors.  
Figure 9 depicts how the F1-scores fluctuate as the number of features used in the model for Quadrant 3 is varied. Notably, the results demonstrate that the model's predictive accuracy remains stable even when the number of features is substantially reduced. Specifically, reducing the feature set from all 49 features to just the top 9 most significant ones does not compromise performance. Furthermore, a model utilizing only 19 features performs on par with one that incorporates the full feature set. Additionally, the model reaches its peak performance by leveraging 42 features with a f1-score of 0.97. These findings suggest that a more streamlined model, which prioritizes the most impactful features, can achieve comparable accuracy to a more complex model that uses a larger array of features.