A study of the role of gesture in the processing of numbers and vectors
Bibliographic record
Abstract
Abstract In this study, we investigated the embodied nature of vector as one of the fundamental concepts in mathematics. Our aim was to examine the role of gesture in finding directions of vectors and magnitudes of vectors on the basis of coordinates of initial points and end points of vectors. In Experiment 1, participants were asked to find directions of vectors on the basis of coordinates of initial points and end points of the vectors. While participants of one group were allowed to gesture, participants of another group were prohibited from gesturing. In Experiment 2, participants were asked to find directions and relative magnitudes of x-coordinates and y-coordinates of vectors on the basis of coordinates of initial points and end points of the vectors. In both experiments, participants of gesture-allowed group had a better performance in answering the questions. Specifically, participants performed better in finding the directions of those vectors which had a left-down direction. Based on these results, it can be concluded that the process of finding the direction and magnitude of a vector on the basis of its initial and end points is mainly embodied as a combination of leftward and downward movements (left-down direction).
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".