Analog games as naturalistic cognitive paradigms: The case of Ghost Blitz and task switching
Bibliographic record
Abstract
Long et al. (2023) recently argued for the gamification of behavioural sciences, where the intrinsic motivations and reward structures associated with games are implemented to improve the quality and quantity of human data. A complementary approach is also available, where commercially available games are studied for their hidden scientific content. Here the goal is not the ‘gamification of science’ but rather the ‘scientification of games.’ This approach asks whether naturalistic data related to the cognitive architecture of human behaviour can be found within games designed for commercial rather than scientific use. As a proof of concept, the card game Ghost Blitz is analysed in both commercialized and non-commercialized formats with respect to the cognitive phenomena of task switching. The scientification of games raises the possibility that the ground truth of behaviour and cognition may be better revealed by treating individuals not as ‘study participants’ but rather ‘game players.’
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".