Neuroticism and Extraversion Magnify Discrepancies Between Retrospective and Concurrent Affect Reports
Bibliographic record
Abstract
OBJECTIVE: Although research often relies on retrospective affect self-reports, little is known about personality's role in retrospective reports and how these converge or deviate from affect reported in the moment. This micro-longitudinal study examines personality (Neuroticism, Extraversion) and emotional salience (peak and recent affect) associations with retrospective-momentary affect report discrepancies over different time frames. METHOD: Participants were 179 adults aged 20-78 (M = 48.7 years; 73.7% Caucasian/White) who each provided up to 60 concurrent affect reports over 10 days, then retrospectively reported overall intensity of each affective state after 1 day and again after 1-2 months. RESULTS: Multilevel models revealed that individuals retrospectively overreported or underreported various affective states, exhibiting peak associations for high arousal positive and negative affect, recency associations for low arousal positive affect, and distinct personality profiles that strengthened over time. Individuals high in both Extraversion and Neuroticism exaggerated high arousal positive and negative affect and underreported low arousal positive affect, high Extraversion/low Neuroticism individuals exaggerated high arousal positive affect and underreported low arousal positive affect, and low Extraversion/high Neuroticism individuals exaggerated high and low arousal negative affect. CONCLUSIONS: This study is the first to identify arousal-specific retrospective affect report discrepancies over time and suggests retrospective reports also reflect personality differences in affective self-knowledge.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".