Immediacy bias in emotion perception: Current emotions seem more intense than previous emotions.
Bibliographic record
Abstract
People tend to perceive immediate emotions as more intense than previous emotions. This immediacy bias in emotion perception occurred for exposure to emotional but not neutral stimuli (Study 1), when emotional stimuli were separated by both shorter (2 s; Studies 1 and 2) and longer (20 min; Studies 3, 4, and 5) delays, and for emotional reactions to pictures (Studies 1 and 2), films (Studies 3 and 4), and descriptions of terrorist threats (Study 5). The immediacy bias may be partly caused by immediate emotion's salience, and by the greater availability of information about immediate compared with previous emotion. Consistent with emotional salience, when people experienced new emotions, they perceived previous emotions as less intense than they did initially (Studies 3 and 5)-a change in perception that did not occur when people did not experience a new immediate emotion (Study 2). Consistent with emotional availability, reminding people that information about emotions naturally decays from memory reduced the immediacy bias by making previous emotions seem more intense (Study 4). Discussed are implications for psychological theory and other judgments and behaviors.
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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 | 0.002 | 0.018 |
| 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) | 0.003 | 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".