Impaired List Learning Is Not a General Property of Frontal Lesions
Bibliographic record
Abstract
BACKGROUND: List-learning tasks are frequently used to provide measures of "executive functions" that are believed necessary for successful memory performance. Small sample sizes, confounding anomia, and incomplete representation of all frontal regions have prevented consistent demonstration of distinct regional frontal effects on this task. OBJECTIVE: To confirm specific effects of lesions in different frontal regions. SUBJECTS: Forty-one patients with chronic focal frontal lesions and 38 control subjects. There were no group differences in naming scores. METHODS: Two word lists were presented, one with unblocked words from related categories and one in a preblocked format. Standard measures of learning, recall, recognition, and strategies were obtained, first for the frontal group as a whole and then for large but defined frontal regions. For all measures with significant group differences, a lesion "hotspotting" method identified possible specific regional injury effects. RESULTS: The frontal group was impaired on almost all measures, but impairments on most measures were particularly identified with lesions in the left superior frontal lobe (approximately area 9s) and some deficits in learning processes were surprisingly more prominent on the blocked list. CONCLUSION: Difficulty with list learning is not a general property of all frontal lesions. Lesions in different frontal regions impair list learning through specific mechanisms, and these effects may be modified by manipulations of the task structure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".