Affective Response Dataset for Virtual Workspaces: Based on Color Stimuli and Multimodal Physiological Signals

Area of Science:

• Human-Computer Interaction
• Affective Computing
• Virtual Environments

Background:

• The normalization of remote work and the rise of the metaverse highlight the importance of virtual workspaces.
• While efficient, virtual workspaces can negatively impact mental health due to a lack of emotional connection.
• Affective computing offers a path to integrate emotional intelligence into virtual environments.

Purpose of the Study:

• To investigate the relationship between color and user emotions within virtual workspaces.
• To establish a foundation for developing emotionally intelligent virtual environments.

Main Methods:

• An experiment was conducted with 48 participants in eight virtual workspaces.
• Four color conditions (red, blue, yellow, green) and two workspace types (shared, single) were used.
• Emotional data were collected via the Positive and Negative Affect Schedule (PANAS), arousal questionnaires, electrodermal activity (EDA), and heart rate variability (HRV).

Main Results:

• Specific color-emotion associations were identified: red with 'anxious', yellow with 'happy', and blue with 'calm'.
• While green did not correlate with a specific emotion, the study successfully classified emotions within virtual workspaces.
• A dataset linking colors, workspace types, and participant emotions was created.

Conclusions:

• Color significantly influences emotional responses in virtual workspaces.
• These findings support the development of affective computing models for enhanced virtual environments.
• The study provides a theoretical basis for designing more emotionally resonant digital spaces.