Sol–Gel Synthesis and Crystal Nucleation of Photosensitive Au/Ag- Containing Glasses of Lithium Silicate System
Bibliographic record
Abstract
Interference between competing motor memories is well-documented in sensorimotor adaptation. Interference is typically assessed using an ABA paradigm wherein participants first adapt to one rotation (Task A), then to an opposite rotation (Task B), and finally are re-exposed to the original rotation (Task A). Interference is observed when performance during the second exposure to Task A is impaired, implying that the motor memory of Task A has been overwritten or masked by the performance of Task B. Previously, we reported that interference is driven by implicit processes because participants exhibited impaired relearning of Task A after adapting to Task B under clamped-feedback conditions (emphasizing implicit processes), but not with delayed-feedback (emphasizing explicit learning). The present study was designed to examine whether memory consolidation, generated by temporal spacing between adaptation sessions, can modulate the interference driven by implicit processes. Participants first adapted to a visuomotor rotation (Task A), returned after 24-hours to complete an opposing clamped rotation (Task B), and then returned another 24-hours later to re-adapt to Task A. It was predicted that the first 24-hr interval would allow for memory consolidation of Task A and reduce the interfering effect of implicit processes. However, results revealed interference during relearning of Task A because there were no differences in performance of Task A between the initial and re-exposure sessions. These findings support the hypothesis that adaptation is particularly susceptible to interference by subsequent implicit learning and highlight the limitations of temporal spacing as a strategy to mitigate interference.
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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 | 0.000 |
| 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".