The emotional impact of interactive cutscene animation on players' empathy - centered on 『The Witcher 3: Wild Hunt』
Bibliographic record
Abstract
This study explores the effects of interactive cut-scene animation on emotions by stimulating player empathy as an example in The Witcher 3: Wild Hunt. The interactive element in the game provides a unique experience in which the story changes depending on the player's decision, which maximizes the player's emotional immersion. This study analyzed the different effects of interactive and non-interactive cut-scene animations on players' empathy and emotional response through randomized controlled experiments. The subjects were randomly assigned to a control group watching non-interactive cut-scene animations and an experimental group participating in interactive cut-scene animations. The emotional state and level of empathy before and after the experiment were evaluated with a questionnaire using the Toronto Empathy Questionnaire (TEQ) and Positive and Negative Affect Schedule (PANAS) scales. The results showed that the interactive cut-scene animation significantly increased the player's level of empathy and positive emotional experience compared to the non-interactive cut-scene animation. These results suggest that the interactive elements in game design play an important role in promoting empathy and immersion. The experiment also confirmed that increased empathy was associated with higher positive emotions, proving that interactivity was effective in strengthening the game emotional experience. This study provides game developers and designers with the basis for designing a more immersive game experience by carefully considering the emotional reactions of players.
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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 | 0.002 |
| 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) | 0.004 | 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".