Revisiting the Impact of Perception on Tasks of Emotionally-Enhanced Vividness
Bibliographic record
Abstract
Previous research has identified that emotional scenes are reported as more vivid than neutral ones, a phenomenon referred to as emotionally-enhanced vividness (EEV). Explanations of EEV point to perceptual or attentional processes for this relationship but did not sufficiently rule out the possibility of a memory bias in reporting the results. To investigate the contribution of perception and memory to tasks examining EEV, we asked participants to view emotionally salient images of negative valence or neutral greyscale images, each with a different level of applied noise as stimuli. After a brief retention interval, the test image was shown onscreen alongside a slider. Participants responded by moving the slider to add noise to the test image until it matched the remembered presentation. In the first experiment, the stimulus and test image were the same. Contrary to previous research, participants in this experiment applied significantly more noise to emotional images compared to neutral images. To elucidate whether this was driven by impaired memory of the stimulus or better perception at test, the second experiment varied the test image independently from the stimulus image. In this experiment, participants rated only neutral stimulus images followed by emotional test images as significantly noisier than any other condition, suggesting that the emotional test image at response was perceived as more vivid than a neutral one. These findings suggest that EEV does occur at the level of perception but that these enhancements are not passed on to subsequent memory for the same scene.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| 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".