What is learned in procedural learning? The case of alphabet arithmetic.
Bibliographic record
Abstract
= 27), adults practiced 12 alphabet arithmetic problems (e.g., C + 3 = C D E F) in two sessions with 20 practice blocks in each. If learning reflected speed up of a counting algorithm, response time (RT) speed up should be proportional to the number of counting steps (+ 1, + 2, or + 3). Instead, we found about 50% of RT gains occurred in the first six blocks of practice during which speed up was parallel for + 1, + 2, and + 3 problems. In both experiments, RT initially was a linear function of addend size, reflecting a letter counting strategy. Mean RT for + 3 problems was eventually equal to + 2 problems, which suggests that speed up reflected a gradual shift to associative fact retrieval. Trial by trial strategy self-reports in Experiment 2 revealed that the proportion of trials reported as memory retrieval as opposed to counting predicted 96% of the variance in RT as a function of addend size and practice block. As such, the results provided no evidence for speed up of a counting algorithm and indicated that skill acquisition for this task entailed speed up of task-general processes independent of addend size and rapid transition from counting to fact retrieval. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".