A Scientific Look at the Design of Aesthetically and Emotionally Engaging Interactive Entertainment Experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The interactive entertainment industry has become a multi-billion dollar industry with revenues overcoming those of the movie industry (ESA, 2009). Beyond the demand for high fidelity graphics or stylized imagery, participants in these environments have come to expect certain aesthetic and artistic qualities that engage them at a very deep emotional level. These qualities pertain to the visual aesthetic, dramatic structure, pacing, and sensory systems embedded within the experience. All these qualities are carefully crafted by the creator of the interactive experience to evoke affect. In this book chapter, the authors will attempt to discuss the design techniques developed by artists to craft such emotionally engaging experiences. In addition, they take a scientific approach whereby we discuss case studies of the use of these design techniques and experiments that attempt to validate their use in stimulating emotions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it