Touching the Virtual Dog: Effects of Active and Passive Haptic Feedback on Social Presence and Emotional Bonding in Virtual Pet Interaction
Bibliographic record
Abstract
Immersive technologies such as virtual and augmented reality (VR/AR) enable people to own and interact with virtual pets, offering an alternative for those who are unable to care for real pets due to spatial, physical, or financial constraints. However, how users form social bonds and emotional connections with virtual pets remains unclear and underexplored. Most virtual pet systems rely primarily on visual and auditory cues, overlooking one critical modality for emotional bonding�touch. Touch plays a fundamental role in emotional communication and social presence, particularly in human-animal interactions. Haptic feedback can fill the role of providing touch cues for users of virtual pet systems. This paper investigates how different haptic feedback modalities can influence emotional communication between users and virtual pets. We compare active haptic feedback�more specifically vibrotactile feedback�delivered through haptic gloves that respond to user interactions with a virtual dog, and passive haptic feedback, provided through a physical plush toy dog that represents the dog's body in physical space. In a within-subjects study with 32 participants, results revealed that passive haptic feedback significantly enhanced emotional bonding, social presence, and perceptions of realism, while active vibrotactile feedback contributed meaningfully in the absence of passive cues, especially in increasing behavioral engagement. These findings offer valuable design insights for emotionally resonant virtual pet systems, suggesting that passive haptics anchor affective realism, while active vibrotactile haptics enhance interactivity and compensate for reduced physical embodiment in immersive environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".