Soccer specific working memory task
Bibliographic record
Abstract
Domain specific cognitive testing demonstrates that individuals who have expertise in a given area perform better at recalling specific details pertaining to that domain. For example, volleyball players can identify if there is a volleyball in a picture (e.g., Starkes & Allard, 1983) better than non-players and chess players relative to non-players are better at recalling chess pieces on a board (e.g., Chase & Simon, 1973). The purpose of the current experiment was to create a task in which we could differentiate soccer players from non-soccer players using a working memory task and compare it to other measures of working memory (i.e., Corsi Block Test). We hypothesized that soccer players would be better at recalling the number of players on the field and would recall the number of players faster than non-soccer players. In order to determine the sensitivity of the protocol 20 participants (10 varsity soccer; 10 non-soccer players) completed a computerized soccer interception task that also embedded questions about the number of players on the field at random times. Results demonstrated that the soccer players were better at determining how many players were on the field. Furthermore, when the performance on a visuospatial working memory test (the Corsi Block Test) was analyzed, no differences were revealed between groups. The results suggest that soccer players perform better on the computerized soccer-specific working memory task and it is not related to an overall better performance on working memory or how fast they respond (no difference in response times between groups). The next step is to determine if this portable task is sensitive to changes in performance resulting from sport-related concussions. Sport specific cognitive tests may help coaches and players make a more direct connection between performance on test and possible deficits on the field of play.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".