Contributions of frontal and medial temporal lobe functioning to the errorless learning advantage
Bibliographic record
Abstract
Among individuals with episodic memory impairments, trial-and-error learning is less successful than when errors are avoided. This "errorless learning advantage" has been replicated numerous times, but its neurocognitive mechanism is uncertain, with existing evidence pointing to both medial temporal lobe (MTL) and frontal lobe (FL) involvement. To test the relative contribution of MTL and FL functioning to the errorless learning advantage, 51 healthy older adults were pre-experimentally assigned to one of four groups based on their neuropsychological test performance: Low MTL-Low FL, Low MTL-High FL, High MTL-Low FL, High MTL-High FL. Participants learned two word lists under errorless learning conditions, and two word lists under errorful learning conditions, and memory was tested via free recall, cued recall, and source recognition. Performance on all three tests was better for those with High relative to Low MTL functioning. An errorless learning advantage was found in free and cued recall, in cued recall marginally more so for those with Low than High MTL functioning. Participants with Low MTL functioning were also more likely to misclassify learning errors as target words. Overall, these results are consistent with a MTL locus of the errorless learning advantage. The results are discussed in terms of the multi-componential nature of neuropsychological tests and the impact of demographic and mood variables on cognitive functioning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".