Selection of Procedures in Mental Division: Relations Between Self-Reports and Eye-Movement Patterns
Bibliographic record
Abstract
Do eye-movement patterns reflect the procedures people use when solving basic arithmetic problems?Sixty-eight adults solved simple division problems while their eye movements were recorded.Thirty-four of these participants reported their solution processes (Experiment 1A: Self-Report Condition) and 34 participants did not (Experiment 1B: Combined Analyses).Participants in Experiment 1A were classified into procedure groups based on their reported use of procedures for large division problems: Retrievers, transformers, and counters.Transformers and counters fixated more on the left and right operands than retrievers for large problems.The 34 participants in Experiment 1B were categorized based on their values of mu and tau for large problems.Patterns of performance for these participants, combined with those who provided self-reports, complemented the patterns found in Experiment 1A.The above results lend support to the use of eye tracking to augment traditional measures of performance when assessing individual differences in procedure selection.
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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 | 0.018 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".