## 2 Literature Review
### Emotion Models  
To understand human emotions, researchers have developed various models, broadly categorized into discrete and dimensional types. Discrete models identify distinct, universally recognized basic emotions, each characterized by unique features [16]. In contrast, dimensional models describe emotions using continuous scales that reflect changes in psychological and physiological parameters, such as valence and arousal [33]. Arousal refers to the intensity of an emotional state, ranging from high to low, while valence describes the emotion's positivity or negativity. Dimensional models can capture the continuous nature of emotional states, which are extensively utilized in research. One prominent dimensional model, the Circumplex Model of Affect (CMA), organizes emotions on a two-dimensional circular plane. The vertical axis represents arousal, and the horizontal axis corresponds to valence. This configuration divides the plane into four quadrants: high arousal/positive valence, low arousal/positive valence, low arousal/negative valence, and high arousal/negative valence [15]. Each of these quadrants represents different emotional states based on their arousal and valence levels. For instance, emotions such as alert, excited, elated, and happy are related to high arousal/positive valence; contented, serene, relaxed, and calm are related to low arousal/positive valence; bored, depressed, and sad emotions are referred to as low arousal/negative valence; and finally, emotions such as upset, stressed, nervous, and tense are related to high arousal/negative valence [34]. The CMA, applied in this study, provides a framework for systematically categorizing emotions and is illustrated in Figure 1.  
This two-dimensional framework provides a clear and intuitive means of mapping a broad spectrum of emotional states, making it particularly useful for studies involving complex and dynamic environments like VR. Its ability to capture a wide range of emotional experiences with relatively few parameters makes it an efficient and effective tool for large-scale data analysis, as required in our study involving eye-tracking. While more recent models have emerged, often incorporating additional dimensions or focusing on specific aspects of emotional experience, the CMA's simplicity and gen eralizability remain advantageous. The model's enduring use in contemporary research attests to its ongoing relevance. For instance, it continues to be cited and applied in studies exploring affective computing, human-computer interaction, and psychological assessments, providing a common framework for comparing results across different studies and contexts. Utilizing these arousal and valence dimensions to measure emotions, [PERSON] et al. [18] integrated facial micro-expressions with EEG and physiological signals collected during a video task designed to elicit six basic emotions and a neutral state. For the analysis, they employed a 3D convolutional neural network (3D-CNN) to classify micro-expression sequences by arousal and valence. Additionally, they used SVM, RF, K-nearest neighbors (KNN), and long short-term memory (LSTM) networks to process other signal data. This multifaceted approach enhances the robustness and accuracy of emotion classification, showcasing the potential of combining multiple data sources and analytical techniques in affective computing. In their innovative work, [PERSON] et al. [25] developed an EEG-based emotion recognition system that utilizes a two-dimensional valence-arousal emotional space to categorize emotions. To enhance accuracy, they utilized the publicly available datasets SEED [35] and DEAP [36], classifying emotions into four distinct classes via SVM, KNN, and naive Bayes (NB). In a related study, [PERSON] et al. [24] explored the efficacy of WPS data in identifying emotions such as anxiety and boredom, employing SVM for their analysis. Furthermore, [PERSON] et al.