How Avatar Customization Affects Fear in a Game-based Digital Exposure Task for Social Anxiety
Bibliographic record
Abstract
The treatment of social anxiety through digital exposure therapy is challenging due to the cognitive properties of social anxiety-individuals need to be fully engaged in the task and feel themselves represented in the social situation; however, avatar customization has been shown to increase both engagement and social presence. In this paper, we harness techniques used in commercial games, and investigate how customizing self-representation in a novel digital exposure task for social anxiety influences the experience of social threat. In an online experiment with 200 participants, participants either customized their avatar or were assigned a predefined avatar. Participants then controlled the avatar through a virtual shop, where they had to solve a math problem, while a simulated audience within the virtual world observed them and negatively judged their performance. Our findings show that we can stimulate the fear of evaluation by others in our task, that fear is driven primarily by trait social anxiety, and that this relationship is strengthened for people higher in trait social anxiety. We provide new insights into the effects of customization in a novel therapeutic context, and embed the discussion of avatar customization into related work in social anxiety and human-computer interaction. ?
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".