Beyond the Uncanny Valley: Attachment Avoidance in User Preferences for AI Virtual Companion Apps Interfaces
Bibliographic record
Abstract
Despite the growing popularity of AI companions, designers have yet to fully consider the critical role of interface modalities in shaping user preferences. We investigate how different interface modalities in AI virtual companion apps (text-based, audio-based, and virtual human-based) influence individual usage intentions. Combining sentiment analysis and the four experiments, we demonstrate that audio-based interfaces elicit the strongest usage intention, outperforming both text-based and virtual human-based interfaces. We also propose that experience perceptions mediate the relationship between interface design and usage intention, highlighting the primacy of psychological factors over visual anthropomorphism—a finding that challenges conventional uncanny valley explanations. Furthermore, attachment avoidance moderates this effect. These findings theoretically advance human-AI virtual companion interaction research by establishing experience perceptions as a mediator and integrating attachment theory. Practically, this study highlights the need for interface designs balancing emotional support with psychological comfort, particularly for users with high attachment avoidance.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".