Press Pause when you Play
Bibliographic record
Abstract
Games allow players to fulfill the need for competence by providing well-designed, increasingly difficult challenges. To meet these challenges, players repeatedly attempt to achieve objectives---and through this repetition, they improve their game skills. Players are keenly aware of whether they are making progress during these attempts, and they want to get better as quickly as possible. Previous research suggests that one way of improving skill development is by taking breaks between periods of activity (called "spaced practice''). However, there is little knowledge about whether this idea works in games, what the optimal break length is, and whether the effects last. We carried out a study comparing spaced and continuous practice in a Super Hexagon clone, using five-minute play intervals and five break lengths (no break, two minutes, five minutes, ten minutes, one day). We found that spaced practice led to significant gains in performance, particularly for novices. This result shows that players can achieve an immediate improvement in skill development, simply by scheduling short breaks in their play session; designers can also make use of this result by building rest periods into the structure of their games. Our study also indicated that breaks are valuable both in the short and the longer term---in a retention test after one day, all of the groups performed similarly, suggesting that even if a player does not use spaced practice initially, taking a break after the play session can still lead to improvements. Our study provides new information that can aid in the design of practice schedules for perceptual-motor tasks in games.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.640 | 0.484 |
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".