Dynamics of the Cognitive Procedural Learning in Alcoholics with <scp>K</scp>orsakoff's Syndrome
Bibliographic record
Abstract
BACKGROUND: While procedures acquired before the development of amnesia are likely to be preserved in alcoholic patients with Korsakoff's syndrome, the ability of Korsakoff patients (KS) to learn new cognitive procedures is called in question. According to the Adaptive Control of Thoughts model, learning a new cognitive procedure requires highly controlled processes in the initial cognitive phase, which may be difficult for KS with episodic and working memory deficits. The goals of the present study were to examine the learning dynamics of KS compared with uncomplicated alcoholic patients (AL) and control subjects (CS) and to determine the contribution of episodic and working memory abilities in cognitive procedural learning performance. METHODS: Fourteen KS, 15 AL, and 15 CS were submitted to 40 trials (4 daily learning sessions) of the Tower of Toronto task (disk-transfer task similar to the tower of Hanoi task) as well as episodic and working memory tasks. RESULTS: The 10 KS who were able to perform the cognitive procedural learning task obtained lower results than both CS and AL. The cognitive phase was longer in the Korsakoff's syndrome group than in the other 2 groups but did not differ between the 3 groups any more when episodic memory abilities were controlled. CONCLUSIONS: Our results indicate that KS have impaired cognitive procedural learning abilities compared with both AL and CS. Episodic memory deficits observed in KS result in a delayed transition from the cognitive learning phase to more advanced learning phases and, as a consequence, in an absence of automation of the procedure within 40 trials.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".