## 3 Methods
### Interaction Dynamic Features  
Based on prior research on social dynamics, we explore the key factors potentially contributing to turn-taking failures across four dimensions: individual status, spatial relationships, interaction proactivity, and communication quality. **Individual Status:** Status disparities among participants are a critical factor influencing conversational equality [65; 66]. For instance, the time interval since a participant's last speech reflects their status: participants who have spoken recently bear greater conversational responsibilities, whereas those who remain silent for extended periods risk marginalization [67]. To capture this characteristic, we introduce the feature _Negative Logarithm of Speaking Interval_, where the time interval refers to the duration between a participant's last speech and their expression of speaking intention. By applying a logarithmic transformation to the time interval, the impact of extreme values is reduced, and taking its negative reflects an inverse relationship, where shorter intervals indicate higher status. **Spatial Relationships:** Spatial relationships play a crucial role in shaping interpersonal interactions [68]. Physical distance has a significant impact on social influence, interaction  
Figure 4: The relationship between the participant-labeled tags and the categories of turn-taking outcomes. frequency [69; 70; 71], as greater distances can weaken social signal transmission [69] and reduce communication efficiency [71]. To quantify spatial relationships, we introduce the feature _Group Area_, defined as the area of the smallest rectangle enclosing the positions of all four participants. This feature provides insights into how spatial distribution influences the fluidity and coordination of turn-taking. **Interaction Proactivity:** Higher interaction proactivity often leads to increased competition for turns and more frequent turn exchanges, thereby complicating turn-taking coordination. The low fidelity of VR environments may exacerbate this issue by reducing participants' ability to detect and respond to non-verbal cues. To measure this dimension, we include two voice-channel-related features: the _Speaking Duration Ratio_ and the _Utterance Count_. These features have been widely used in previous studies to assess conversational activity and interaction patterns [32; 72; 73]. Additionally, to complement the measurement of interaction intensity from a non-verbal perspective, we calculate the _Speaking Intention Count_, which represents the number of speaking intention tags from others. This feature reflects the active expression of speaking intentions, providing additional support for a comprehensive assessment of interaction proactivity. **Communication Quality:** Communication quality is closely related to the effectiveness of interactions. Clear expression reduces misunderstandings among participants, enhancing the accuracy and consistency of information exchange. Balanced participation fosters diverse viewpoints and cultivates an open and inclusive conversational environment. To measure differences in communication quality, we introduced two key features: _Speaking Overlap Ratio_ and _Max Speaker Duration Ratio_. The speaking overlap ratio represents the proportion of overlapping speaking time to the total speaking time, reflecting the level of conversational chaos, as simultaneous speech by multiple participants can hinder effective information exchange. The max speaker duration ratio measures the ratio of the longest speaker's speaking duration to the total speaking time, indicating the balance of participation. A lower ratio suggests a more open and balanced communication, which we hypothesize may facilitate the acceptance of participants' speaking intentions. Finally, Table 1 provides a summary of all features. Table 2 provides the descriptive statistics for each feature. To calculate the relevant features, we selected data within a time window of \(t\) seconds before and after participants annotated their speaking intentions.