Training cognition: Parallels with physical fitness?
Bibliographic record
Abstract
In their article on memory training, McDaniel and Bugg (2012) rst criticize the idea that any type of memory training will genralize to all other types; the notion that memorizing poems or peeches from plays will improve a person’s memory for names or umbers, for example. We strongly agree with this criticism. Cogitive processes resemble physical skills in many respects, and few eople would expect that hours of tennis practice would improve heir golf game, or even that putting practice would improve drivng off the tee. Memory is not one monolithic faculty. The authors f the target article then go on to advocate training methods that mphasize more specific aspects of memory performance, such s prospective memory, retrieval and recollection. We agree that his would be extremely valuable, but are somewhat skeptical that ell attested methods exist at present (see Reichman, Fiocco, & ose, 2010 for a recent review). Training strategies, with practice t applying relevant strategies to real-life problems, appears tohold ut more promise for older adults, although again the present evience is sparse. We await with interest the results of the authors’ XACT trial. We certainly hope and expect that findings from laboratory tudies of memory can be applied successfully to training regimes or older adults. McDaniel and Bugg suggest that the principles f spacing, variation and interleaving practice with various tasks nd strategiesmay be helpful. Although they tend to lump strategy
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".