A Change of Scenery: Transformative Insights from Retrospective VR Embodied Perspective-Taking of Conflict With a Close Other

TL;DR

This paper addresses the difficulty of resolving conflict in close relationships by introducing RETROSPECTIVE Embodied Perspective-Taking (REPT), a VR-based method that lets a user inhabit their partner's perspective during a past conversation. In a mixed-methods, between-subjects study with 26 romantic dyads, REPT outperformed traditional video-based reflection (TAU) in promoting moment-to-moment, subjective reflections and yielding transformative insights, along with notable improvements in several communication-related dimensions as measured by IDCS. Although empathic accuracy did not significantly differ, REPT effectively reduced negative affect and conflict indicators while boosting positive communication and support, suggesting that embodied experience can alter attribution patterns and foster cognitive empathy in familiar relationships. The findings contribute to an emerging design space for embodied social cognition, proposing that embodied experiences can serve as a durable interaction context for social reflection and conflict resolution with close others, with implications for future VR design and therapy tools.

Abstract

Close relationships are irreplaceable social resources, yet prone to high-risk conflict. Building on findings from the fields of HCI, virtual reality, and behavioral therapy, we evaluate the unexplored potential of retrospective VR-embodied perspective-taking to fundamentally influence conflict resolution in close others. We develop a biographically-accurate Retrospective Embodied Perspective-Taking system (REPT) and conduct a mixed-methods evaluation of its influence on close others' reflection and communication, compared to video-based reflection methods currently used in therapy (treatment as usual, or TAU). Our key findings provide evidence that REPT was able to significantly improve communication skills and positive sentiment of both partners during conflict, over TAU. The qualitative data also indicated that REPT surpassed basic perspective-taking by exclusively stimulating users to embody and reflect on both their own and their partner's experiences at the same level. In light of these findings, we provide implications and an agenda for social embodiment in HCI design: conceptualizing the use of `embodied social cognition,' and envisioning socially-embodied experiences as an interactive context.

Figures (5)

• Figure 1: Flow diagram of study procedure including distribution of conditions, sessions, and task order between participants (both members of a couple). Discussions 1 and 2 occurred between both partners, while the other tasks were completed separately. See \ref{['session1']} for details on Partner A and B.
• Figure 2: REPT (VR) and TAU (split-view video) recording setup and resulting views. (a) REPT condition recording setup with Partner B wearing the head-mounted camera. The TAU condition setup was exactly the same but without the head-mounted camera. (b) The REPT condition view extracted from the 360° recording, seen during Partner A's perspective-taking of Partner B. (c) The TAU condition view, with both partners visible.
• Figure 3: Visual guide of each axial theme from Partner A's reflective interview. In each subsection of Section \ref{['sec:findings']}, we define criteria for the reflection property in each axial sub-theme and report the counts of participants across REPT and TAU conditions who exhibited each property during the interview; salient differences across conditions are grouped to left and right.
• Figure 4: Mean change in IDCS individual dimension scores from Discussion 1 to Discussion 2, shown across REPT and TAU conditions separately for Partner A and Partner B (See \ref{['fig:procedure']} for study procedure). X-axis specifies the change in score; Y-axis specifies IDCS individual dimensions in the following top-down order: Positive Affect, Problem Solving, Support/Validation, Communication Skills, Negative Affect, Denial, Withdrawal, Dominance, Conflict (See \ref{['tab:idcs']} for definitions).
• Figure 5: Sensitivity power analysis with our sample size $N = 13$ and significance level $\alpha = 0.05$. With desired power of $0.8$, the predicted effect size under these assumptions is $d = 1.145$. With power $0.7$ the predicted effect size is $d = 1.015$.