Exploring Emotions in Multi-componential Space using Interactive VR Games

Główne pojęcia

Emotions are complex, multi-faceted experiences that involve multiple interconnected components, including appraisal, motivation, expression, physiology, and feeling. This study explores the relationship between the Component Process Model (CPM) and discrete emotions using interactive Virtual Reality (VR) games, multimodal data collection, and machine learning methods.

Streszczenie

The study aimed to better understand the role of each CPM component in capturing emotions using self-reports, physiological signals, and facial signals. The researchers also explored the potential of VR games to induce a range of emotions with different intensities.
Key highlights:

VR games were effective in eliciting a wide range of emotions, including both positive and negative emotions, though the intensity of some complex emotions like sadness, guilt, and contempt was relatively low.
Hierarchical clustering of the CPM profiles of discrete emotions revealed a clear distinction between positive and negative emotions, with further sub-clusters corresponding to different emotional categories.
Factor analysis identified five latent dimensions underlying the emotional experience, including aspects related to body changes, valence, agency, novelty, and norms.
Facial EMG signals showed significant correlations with various discrete emotions and CPM expression components, highlighting the role of facial expressions in emotion formation.
Machine learning models using the full CPM, including self-reports and physiological/facial signals, demonstrated the best performance in differentiating discrete emotions, outperforming models using individual components.
The appraisal, motivation, and expression components were found to be the most significant in distinguishing discrete emotions, while the feeling component showed limited predictive power, especially for negative emotions.
The findings provide insights into the complex nature of emotions and the importance of considering multiple components in emotion recognition. The use of interactive VR environments and multimodal data collection offers a more realistic and comprehensive approach to studying emotional experiences.

Statystyki

The intensity of different CoreGRID items varied in response to different discrete emotions or combinations of them.
Smile EMG was positively correlated with interest, joy, pleasure, amusement, pride, and fear, and negatively correlated with admiration, sadness, and contempt.
Frown EMG was positively correlated with most negative emotions like disgust, hate, fear, and anger.
Eyebrow raise EMG showed ambiguous patterns, with positive correlations with admiration, compassion, and negative correlations with interest and pleasure.

Cytaty

"Emotions are complex cultural and psychobiological states that are an important aspect of human experience and play a central role in our social interactions and decision-making."
"Renewed interest in the CPM has led to data-driven analyses. However, these studies have often been limited in their use of active participation, objective measures of the physiology and expression components, and the induction of a broader range of emotions."
"Our findings inform the role of each CPM component in capturing emotions. These insights can be used for enhanced context awareness, system interaction, and system adaptations in domains including healthcare, VR environments, education, gaming, and interfaces."

Kluczowe wnioski z

Exploring Emotions in Multi-componential Space using Interactive VR Games

by Rukshani Som... o arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03239.pdf

Głębsze pytania

How can the findings from this study be applied to develop more emotionally intelligent and adaptive systems, such as virtual assistants or therapeutic applications?

The findings from this study can be instrumental in developing emotionally intelligent and adaptive systems by providing insights into how different components of emotions interact and contribute to the overall emotional experience. By understanding the complex and multi-faceted nature of emotions, developers can design systems that can recognize, interpret, and respond to a wider range of emotions. For virtual assistants, this could mean incorporating algorithms that can analyze facial expressions, physiological signals, and self-reported data to better understand the user's emotional state and tailor responses accordingly. In therapeutic applications, these insights can be used to create immersive VR experiences that help individuals explore and manage their emotions in a controlled environment.

What are the potential limitations of using VR games to induce emotions, and how can these limitations be addressed in future research?

One potential limitation of using VR games to induce emotions is the limited range of emotions that can be effectively elicited, as some complex or socially oriented emotions may be challenging to evoke in a gaming context. Additionally, individual differences in emotional responses to VR stimuli can impact the generalizability of the findings. To address these limitations in future research, a more diverse set of VR experiences can be developed to target a broader range of emotions, including those that are more nuanced or socially oriented. Incorporating personalized feedback mechanisms based on individual emotional responses can also enhance the effectiveness of emotion induction in VR games.

How might the insights from this study on the multi-faceted nature of emotions inform our understanding of human decision-making and social interactions in the real world?

The insights from this study on the multi-faceted nature of emotions can provide valuable information on how emotions influence human decision-making and social interactions in the real world. Understanding the interconnected components of emotions, such as appraisal, motivation, expression, physiology, and feeling, can shed light on the underlying processes that drive human behavior. By recognizing the complexity and variability of emotions, we can better comprehend how individuals perceive and respond to different situations, make decisions, and interact with others. This knowledge can be applied in various fields, such as psychology, human-computer interaction, and social sciences, to improve communication, empathy, and overall well-being in society.