Expert-Novice Differences in Memory: A Reformulation
Bibliographic record
Abstract
BACKGROUND: One of the most discriminating measures of expertise in multiple domains has been performance on memory tasks. In medicine, however, the relation between expertise and memory is more equivocal. PURPOSE: To compare and contrast the sufficiency of multiple explanations of this finding by using three probes of memory rather than the traditional free recall task alone. METHODS: Students, residents, and internists were asked to read case histories and assign diagnoses before undertaking free recall, cued recall, and recognition tests. RESULTS: Students consistently outperformed internists. Resident performance was more variable. CONCLUSIONS: Our data appear to rule out (a) the notion that expert memory for cases takes on an encapsulated form, (b) the idea that experts simply say less than students in response to a free recall task, and (c) the possibility that experts attend differentially to highly diagnostic features. The results can best be explained by the idea that students process the featural details of a case history more elaborately than do expert diagnosticians who, instead, read medical cases more holistically.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".