The effects of post‐learning alcohol ingestion on human motor memory consolidation
Bibliographic record
Abstract
Abstract The neurochemical mechanisms underlying motor memory consolidation remain largely unknown. Based on converging work showing that ethyl alcohol retrogradely enhances declarative memory consolidation, this work tested the hypothesis that post‐learning alcohol ingestion would enhance motor memory consolidation. In a within‐subject and fully counterbalanced design, participants ( n = 24; 12M; 12F) adapted to a gradually introduced visual deviation and ingested, immediately after adaptation, a placebo (PBO), a medium (MED) or high (HIGH) dose of alcohol. The alcohol doses were bodyweight‐ and gender‐controlled to yield peak breath alcohol concentrations of 0.00% in the PBO, ~0.05% in the MED and ~0.095% in the HIGH condition. Retention was evaluated 24 h later through reach aftereffects when participants were sober. The results revealed that retention levels were neither significantly nor meaningfully different in both the MED and HIGH conditions as compared to PBO (all absolute Cohen's d z values < ~0.2; small to negligible effects), indicating that post‐learning alcohol ingestion did not alter motor memory consolidation. Given alcohol's known pharmacological GABAergic agonist and NMDA antagonist properties, one possibility is that these neurochemical mechanisms do not decisively contribute to motor memory consolidation. As converging work demonstrated alcohol's retrograde enhancement of declarative memory, the present results suggest that distinct neurochemical mechanisms underlie declarative and motor memory consolidation. Elucidating the neurochemical mechanisms underlying the consolidation of different memory systems may yield insights into the effects of over‐the‐counter drugs on everyday learning and memory but also inform the development of pharmacological interventions seeking to alter human memory consolidation.
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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 | 0.000 | 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) | 0.002 | 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".