Expert vs. novice differences in the detection of relevant information during a chess game: evidence from eye movements
Bibliographic record
Abstract
The present study explored the ability of expert and novice chess players to rapidly distinguish between regions of a chessboard that were relevant to the best move on the board, and regions of the board that were irrelevant. Accordingly, we monitored the eye movements of expert and novice chess players, while they selected white's best move for a variety of chess problems. To manipulate relevancy, we constructed two different versions of each chess problem in the experiment, and we counterbalanced these versions across participants. These two versions of each problem were identical except that a single piece was changed from a bishop to a knight. This subtle change reversed the relevancy map of the board, such that regions that were relevant in one version of the board were now irrelevant (and vice versa). Using this paradigm, we demonstrated that both the experts and novices spent more time fixating the relevant relative to the irrelevant regions of the board. However, the experts were faster at detecting relevant information than the novices, as shown by the finding that experts (but not novices) were able to distinguish between relevant and irrelevant information during the early part of the trial. These findings further demonstrate the domain-related perceptual processing advantage of chess experts, using an experimental paradigm that allowed us to manipulate relevancy under tightly controlled conditions.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".