Bibliographic record
Abstract
In multiplayer First-Person Shooter (FPS) games, experience can suffer if players have different skill levels -- novices can become frustrated, and experts can become bored. An effective solution to this problem is aiming-assistance-based player balancing, which gives weaker players assistance to bring them up to the level of stronger players. However, it is unknown how assistance affects skill development. The guidance hypothesis suggests that players will become overly reliant on the assistance and will not learn aiming skills as well as they would without it. In order to determine whether aiming assistance hinders FPS skill development, we carried out a study that compared performance gains and experiential measures for an assisted group and an unassisted group, over 14 game sessions over five days. Our results show that although aim assistance did significantly improve performance and perceived competence when it was present, there were no significant differences in performance gains or experiential changes between the assisted and unassisted groups (and on one measure, assisted players improved significantly more). These results go against the prediction of the guidance hypothesis, and suggest instead that the value of aiming assistance outweighs concerns about skill development -- removing one of the remaining barriers that designers may see in using player balancing techniques.
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) | 0.012 | 0.003 |
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; both teacher heads agree on what is shown here.
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".