Exploring the generalisation of affect across related experiences: A study of affective bleed and memory precision
Bibliographic record
Abstract
When positive or negative events occur in a context, memory can be reflected in how positively or negatively we judge that context, and also by whether, upon later remembering that emotional event, we can bring to mind the specific context in which it occurred. We examined each of these forms of associative memory, comparing performance when positive, negative, or neutral stimuli were paired with a context. By doing so, we could contribute to debates about how emotion affects associative binding. Participants intentionally formed associations between famous places and positive, negative, or neutral pictures. In Experiment 1, we observed shifts in judgments for places as a function of associated valence; effects summated over accumulated experiences. In Experiment 2, memory precision was examined by manipulating whether lures on a five-alternative forced-choice recognition, included different places or alternate views of the target. Results revealed emotional impairments in associative memory and a selective decrease in precision for negative pairs. Eye-tracking showed more saccades between pictures for remembered pairs, but less of these inter-item saccades when pictures were emotional. Overall findings suggest that positive and negative affect are transferred similarly through episodic associations, although the specificity of context transfer may be lessened for negative content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".