The retrograde effects of negative emotion on memory for conceptually related events: a registered report
Bibliographic record
Abstract
Emotional events are often remembered better than neutral ones; however, emotion can also spill over and affect our memory for neutral experiences that precede an emotional event. Theories suggest that emotion can retroactively enhance memory for preceding neutral events that are considered high-priority while impairing memory for events deemed low-priority. However, the impact of conceptual relationships (i.e., semantic connections) between preceding neutral information and emotional events on memory for the preceding information has received little attention. This study investigated the influence of conceptual relatedness on the retroactive effects of emotion on memory. Participants sequentially encoded pairs of images that were high or low in conceptual relatedness, each comprising a neutral object followed by either a negative or neutral image. Participants returned the next day for a recognition memory assessment. The results indicated an interactive effect of emotion and conceptual relatedness on memory: In a "discovery" sample, memory was poorer for images preceding conceptually unrelated negative (vs. neutral) images, while the opposite pattern was seen for conceptually related images. In a "replication" sample, these effects were partially replicated, with the former impairment effect statistically observed but not the latter augmentation effect. Hence, conceptual relatedness affects how negative emotion influences memory.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".