Evaluating commercial game design decisions via the scientification of games: Asymmetrical task switching costs predict self-reported fun in Ghost Blitz
Bibliographic record
Abstract
By decomposing the structure, rule set and stimuli of games, it becomes possible to examine the impact of specific choices made by designers and publishers: not the ‘gamification of science’ but rather the ‘scientification of games.’ Here, the card game Ghost Blitz was analysed using both commercialized (cartoon illustrations) and a more ‘experiment-like’ (abstract shapes) format, where each card required players to search according to either the presence (Task A) or absence (Task B) of visual features. Thus, this game can be used to both demonstrate and study the cognitive phenomena of visual search asymmetry and task switching. The commercial format generated more fun and produced faster reaction times than the ‘experiment-like’ format, demonstrating the importance of surface characteristics. The original version of Ghost Blitz (where Task B was more frequent) was rated as less fun than an inversed version (where Task A was more frequent), highlighting the importance of structural characteristics. This surprising result was explained via multiple regression, where the frequency with which players experience accuracy loss during Task B to Task A switching predicted the reduction in self-reported fun. By meeting people where they are, games allow the public to have increased connection with psychological theory and enable the empirical validation of choices made during commercial game design.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".