A negativity bias in detail generation during event simulation.
Bibliographic record
Abstract
Novel negative events are simulated in more event-specific detail than novel positive events. In the present study, we set out to assess whether this negative event detail bias is specific to simulations of personal events or whether evoking negative valence, in the context of simulations of personal and nonpersonal events, is sufficient for boosting simulated event detail. Participants simulated novel negative and positive events that might take place in their future, the future of an acquaintance, or the future of a familiar individual with whom they have not had prior contact. Across 2 experiments, we found that novel negative events were simulated in more event-specific detail than novel positive events irrespective of whether the events under consideration were personal or nonpersonal. This pattern of results also emerged when negative and positive events did not differ on a subjective measure of arousal, indicating that negative valence may play a key role in encouraging detailed elaboration of novel negative events. Implications of our findings for the role of event simulation in adaptive behavior are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".