Working memory strategies during rational number magnitude processing.
Bibliographic record
Abstract
Rational number understanding is a critical building block for success in more advanced mathematics; however, how rational number magnitudes are conceptualized is not fully understood. In the current study, we used a dual-task working memory (WM) interference paradigm to investigate the dominant type of strategy (i.e., requiring verbal WM resources vs. requiring primarily visuospatial WM resources) used by adults when processing rational number magnitudes presented in both decimal and fraction notation. Analyses revealed no significant differences in involvement of verbal and visuospatial WM, regardless of notation (fractions vs. decimals), indicating that adults rely upon a mix of strategies and WM resources when processing rational number magnitudes. However, this pattern interacted with algebra ability such that those performing better on the algebra assessment relied upon both verbal and visuospatial WM when engaging in rational number comparisons, whereas rational number performance by adults with low algebra fluency was affected only by a simultaneous verbal WM task. Together, results support previous work implicating the involvement of WM resources in rational number processing and is the first study to indicate that the involvement of both verbal and visuospatial WM, as opposed to relying primarily on verbal WM, when processing rational number magnitudes may be indicative of higher mathematical proficiency in the domain of algebra. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".