This is your brain on Scrabble: Neural correlates of visual word recognition in competitive Scrabble players as measured during task and resting-state
Bibliographic record
Abstract
Competitive Scrabble players devote considerable time to studying words and practicing Scrabble-related skills (e.g., anagramming). This training is associated with extraordinary performance in lexical decision, the standard visual word recognition task (Hargreaves, Pexman, Zdrazilova & Sargious, 2012). In the present study we investigated the neural consequences of this lexical expertise. Using both event-related and resting-state fMRI, we compared brain activity and connectivity in 12 competitive Scrabble experts with 12 matched non-expert controls. Results showed that when engaged in the lexical decision task (LDT), Scrabble experts made use of brain regions not generally associated with meaning retrieval in visual word recognition, but rather those associated with working memory and visual perception. The analysis of resting-state data also showed group differences, such that a different network of brain regions was associated with higher levels of Scrabble-related skill in experts than in controls.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".