Imagery and Emotion Components of Event Descriptions about Self and Various Others
Bibliographic record
Abstract
Imagery and emotion have been identified as two of the main component systems of autobiographical events. It is not yet known, however, whether a primary focus on either the self or others may have an impact on these components. To investigate this issue, half of the participants in this study provide a real and made-up event description about themselves, and half provide descriptions about a well-known other. In addition, all participants generated a made-up event description about an unfamiliar other. In accord with predictions generated from a multiple-system model, real events received higher visual detail, imagability, and positive emotion ratings than made-up events. This pattern was also evident for a novel measure of imagery, in which real events were rated as being much more dynamic than made-up events. However, contrary to theoretical positions which postulate a special enhanced role for self-referent information processing, the self-descriptive events were not rated as being easier to imagine and did not have more positive emotions or visual detail, than descriptive events about well-known others. This pattern suggests that efficient cognitive schemata may be involved in the processing of information about both the self and well-known others. In contrast, descriptions of an unfamiliar other received lower imagery and emotionality ratings, suggesting that less well-differentiated cognitive structures are involved in component processing for these individuals.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".