Selection of procedures in mental subtraction: Use of eye movements as a window on arithmetic processing.
Bibliographic record
Abstract
Adults who use mental procedures other than direct retrieval to solve simple arithmetic problems typically make more errors and respond more slowly than individuals who rely on retrieval. The present study examined how this extra time was distributed across problem components when adults (n = 40) solved small (e.g., 5 - 2) and large (e.g., 17 - 9) subtraction problems. Two performance groups (i.e., retrievers and procedure users) were created based on a 2-group cluster analysis using statistics derived from the ex-Gaussian model of reaction time (RT) distributions (i.e., μ and τ) for both small and large problems. Cluster results differentiated individuals based on the frequency with which they used retrieval versus procedural strategies, supporting the view that differences in mu and tau reflected differences in choice of strategies used. Patterns of eye movements over time were also dramatically different across clusters, and provide strong support for the view that individuals were using different mental procedures to solve these problems. We conclude that eye-movement patterns can be used to distinguish fluent individuals who readily use retrieval from those who rely more on procedural strategies, even if traditional self-report methods are unavailable. (PsycINFO Database Record
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".