The scientification of games: Analysing the card game Ghost Blitz through the lens of Cognitive Psychology
Bibliographic record
Abstract
The gamification of science implements aspects of game design into scientific paradigms, overcoming the pitfalls associated with laboratory-based data collection. We identify the complementary route of the scientification of games, where the study of commercially developed games provides novel insights into behavioural science. Using the card game Ghost Blitz, we identify how players might resolve the game on a round-by-round basis via both bottom-up (stimulus-driven) and top-down (expectation-driven) processes. We identify statistical biases within the game favouring one rule over the other, and, a second bias where specific stimuli are over-represented. These detailed analyses allow for a re-designed and balanced version of the game, incorporating elements of feature versus conjunction processing, visual search asymmetries, and, task switching. The scientification of games allows for unique teachable moments using games as the vehicle of delivery, and, feeds back principles of randomization and counterbalancing into the design of commercial games.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".