The roles of deliberate practice and innate ability in developing expertise: evidence and implications
Bibliographic record
Abstract
CONTEXT: Medical education research focuses extensively on experience and deliberate practice (DP) as key factors in the development of expert performance. The research on DP minimises the role of individual ability in expert performance. This claim ignores a large body of research supporting the importance of innate individual cognitive differences. We review the relationship between DP and an innate individual ability, working memory (WM) capacity, to illustrate how both DP and individual ability predict expert performance. METHODS: This narrative review examines the relationship between DP and WM in accounting for expert performance. Studies examining DP, WM and individual differences were identified through a targeted search. RESULTS: Although all studies support extensive DP as a factor in explaining expertise, much research suggests individual cognitive differences, such as WM capacity, predict expert performance after controlling for DP. The extent to which this occurs may be influenced by the nature of the task under study and the cognitive processes used by experts. The importance of WM capacity is greater for tasks that are non-routine or functionally complex. Clinical reasoning displays evidence of this task-dependent importance of individual ability. CONCLUSIONS: No single factor is both necessary and sufficient in explaining expertise, and individual abilities such as WM can be important. These individual abilities are likely to contribute to expert performance in clinical settings. Medical education research and practice should identify the individual differences in novices and experts that are important to clinical performance.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".