Temporally graded impairment of retention induced by prior learning of the same motor task
Bibliographic record
Abstract
Learning rarely occurs in isolation from previous experiences. In fact, the brain's response to an ongoing event depends on its previous history of activity. Namely, neurobiological evidence indicates that previous learning, by disrupting the brain's homeostatic state, can transiently saturate neuroplastic capacity, thus potentially impairing subsequent retention capacities. The objective of the present work was to test this hypothesis. Three distinct experiments were conducted in which participants (n = 124) adapted twice to the same gradually introduced 21° visuomotor rotation over two separate sessions. Retention was assessed through extinction of adapted behaviors upon removal of the rotation immediately after adaptation. Globally, results revealed that when the two sessions were interleaved with 2 or 12min, but not with 1 or 24h, retention of the second session was impaired as compared to the first one, suggesting a temporally graded saturation of retention capacities by previous learning. Furthermore, putatively inhibitory and excitatory repetitive transcranial magnetic stimulation (rTMS) protocols were applied over M1 during the 12min inter-session interval to modify the history of brain activity with the objective of restoring subsequent retention. Although the rTMS protocols effectively modulated M1 activity, they failed to alter subsequent retention capacities, suggesting that previous learning-induced homeostatic state disruption may be refractory to the effects of rTMS over M1. Globally, the present results indicate that the brain's retention capacities can be impaired by its previous history of learning of the same task and that the passage of time may remain, yet, the best way to restore retention capacities.
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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 | 0.000 |
| 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 | 0.001 |
| 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".