Designing for Emotional Complexity in Games
Bibliographic record
Abstract
People play games for the experience, and one of the aims of player experience research is to understand what constitutes and contributes to positive gaming experiences. Emotionally challenging and uncomfortable game play experiences have been largely neglected, as they are seemingly at odds with the field's focus on fun and positive affect. We argue that the positively-biased perspective on desirable emotions in games misses out on opportunities that the interplay between positive and negative emotions offers. A previous workshop at CHI PLAY 2015 covered this missed opportunity by focusing on the false dichotomy between positive and negative affect, and identified a number of factors, both personal and contextual, which determine when players will value emotional game experiences that go beyond the purely positive. The present workshop is a continuation of this effort, putting the spotlight on the complexity of emotional experience and how it evolves throughout game play. Crucially, a central aspect of this workshop is to get participants thinking more about the design and evaluation of these types of experiences, by allowing hands-on game design exercise for the examined emotional experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".