Are Real Moods Required to Reveal Mood-Congruent and Mood-Dependent Memory?
Bibliographic record
Abstract
While simulating, or acting as if, they were either happy or sad, university students recounted emotionally positive, neutral, or negative events from their personal past. Two days later, subjects were asked to freely recall the gist of all of these events, and they did so while simulating a mood that either did or did not match the one they had feigned before. By comparing the present results with those of a previous study, in which affectively realistic and subjectively convincing states of happiness and sadness had been engendered experimentally, we searched for--and found--striking differences between simulated and actual moods in their impact an autobiographical memory. In particular, it appears that the mood-congruent effects elicited by simulated moods are qualitatively different from those evoked by induced moods, and that only authentic affects have the power to produce mood-dependent effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".