Intimidating or Friendly? How Players Represent Themselves With Character Appearances That Reflect Their Social Motivations
Bibliographic record
Abstract
Games often allow players to customize their virtual representations, and players tend to restrict themselves to appearances that are considered socially acceptable. However, there are different ideas of what a desirable appearance is (e.g., strong versus cute) and different players have different preferences. Through two studies, we explore the link between self-reported motivations (i.e., explicit motives—affiliation, power, and achievement) and the desirability of various appearance attributes. The affiliation motive is associated with striving for approachable appearances, such as characters with friendly facial expressions. The power motive predicts the goal to create strong, mighty, and less approachable appearances, and achievement-motivated individuals are more likely to seek out unremarkable appearances. Affiliation-motivation positively predicted time spent in character customization and creating characters in line with beauty standards (i.e., thin characters). We further observe that certain players are influenced by character appearance when making playstyle decisions. The results indicate that appearance preferences are diverse but not random and that players are more likely to pick avatars and playstyles that communicate their social motivations to other players. Theoretical explanations from the perspective of Motive-Disposition-Theory are discussed.
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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 | 0.006 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".