Emotion enhanced retention of cognitive skill learning.
Bibliographic record
Abstract
Ample evidence suggests that emotional arousal enhances declarative/episodic memory. By contrast, there is little evidence that emotional enhancement of memory (EEM) extends to procedural skill based memory. We examined remote EEM (1.5-month delay) for cognitive skill learning using the weather prediction (WP) probabilistic classification task. Participants viewed interleaved emotionally arousing or neutral pictures during WP acquisition. Arousal retarded initial WP acquisition. While participants in the neutral condition showed substantial forgetting of WP learning across the 1.5-month delay interval, the arousal condition showed no evidence of forgetting across the same time period. Thus, arousal during encoding determined the mnemonic fate of cognitive skill learning. Emotional enhancement of WP retention was independent of verbally stated knowledge of WP learning and EEM for the picture contexts in which learning took place. These results reveal a novel demonstration of EEM for cognitive skill learning, and suggest that emotional arousal may in parallel enhance the neural systems that support procedural learning and its declarative context.
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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) | 0.002 | 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".