Bibliographic record
Abstract
The first sentence of the introduction to Hebb's (1949) classic monograph, The organization of behavior, is "It might be argued that the task of the psychologist, the task of understanding behaviour and reducing the vagaries of human thought to a mechanical process of cause and effect, is a more difficult one than that of any other scientist" (p. xi). Nowhere is this more true than in the realm of human learning and memory, given our truly remarkable ability to acquire and retain prodigious amounts of information. This article is divided into two parts. The first part sketches my lifelong fascination with learning that led me to study first memory, then attention, and then their interplay, with examples of a few interesting findings along that path. The second part details recent work in my laboratory exploring a simple yet quite powerful encoding technique: Saying things aloud improves memory for them. This benefit, which we call the production effect, likely occurs by enhancing the distinctiveness of the things said aloud, and may constitute a beneficial study method. Understanding how we learn and remember is ultimately a crucial step in understanding ourselves.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".