Robustness of performance during domain change in an esport: A study of within-expertise transfer
Bibliographic record
Abstract
Research on the transfer of skill from the circumstances in which it was learned to partially or completely novel tasks or situations is a foundational topic in the study of learning, memory, education, and expertise. A long history of transfer research has led to the conclusion that skill learning is generally domain specific. One important transfer problem occurs when a domain of expertise undergoes a fundamental shift, as when experts must adapt to changes in technology, rules, or professional practice. Here we examine skill maintenance in StarCraft 2, a video game of skills which undergoes frequent changes due to updates and includes a variety of gameplay options. Of particular interest are two competing predictions about how transfer will interact with expertise in this domain. The first approach emphasizes perceived similarity of the domains and predicts that skilled individuals will exhibit more favourable transfer than novices as these people will know enough to avoid processes, methods, and strategies which no longer apply after a domain change. The second emphasizes maximal adaptation to task constraints and predicts that experts will suffer the most during a domain change because of the loss of exploitable affordances. Neither approach did a good job explaining behaviour after the major game update called 'StarCraft 2: Heart of the Swarm,' perhaps because transfer was generally strong across all players. However, when examining transfer in the context of larger changes to gameplay, transfer seemed slightly better in more experienced players. The theoretical implications of this apparent interaction effect, and of the apparent resilience of more experienced StarCraft 2 players to transfer costs, are discussed.
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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 | 0.021 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".