Neutral by Default? Replicating User Vocal Responses to Negative Affective Cues in Conversational Agents

Conversational agents (CAs) increasingly detect users’ emotions, yet deciding how to respond, especially to negative affect, remains a central design challenge. We conducted a role-switching study in which participants reply as the CAs to simulated users expressing anger, sadness, or fear. Results reveal systematic, gender-linked patterns: most male participants favored a neutral, affect-balanced stance and prioritized clarification or task progress, whereas most female participants produced a wider range of non-neutral responses, more often using explicit empathy, reassurance, and reflective listening. We also observe differences in de-escalation phrasing, validation timing, and follow-up questioning across scenarios. These findings indicate that strategies for handling negative emotions vary with user characteristics and context. Based on these findings, we argue for adaptive CA response policies that calibrate first-turn acknowledgment and information-gathering, tailoring prosody and wording to emotional context in order to support de-escalation, perceived understanding, and user trust.