Bibliographic record
Abstract
We reviewed the literature on the role of working memory in the solution of arithmetic problems such as 3 + 4 or 345 + 29. The literature was neither comprehensive nor systematic, but a few conclusions are tenable. First, all three components of the working memory system proposed by Baddeley (i.e., central executive, phonological loop, and visual‐spatial sketchpad) play a role in mental arithmetic, albeit under different conditions. Second, mental arithmetic requires central executive resources, even for single‐digit problems. Third, further progress in understanding the role of working memory in arithmetic requires that researchers systematically manipulate factors such as presentation conditions (e.g., operand duration, format), problem complexity, task requirements (e.g., verification vs production), and response requirements (e.g., spoken vs written); and that they consider individual differences in solution procedures. Fourth, the encoding‐complex model (Campbell, 1994) seems more likely to account for the variability observed in arithmetic solutions than other models of numerical processing. Finally, working memory researchers are urged to use mental arithmetic as a primary task because the results of the present review suggest that solution of problems that involve multiple digits are likely to involve an interaction of all the components of the working memory system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 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".