A Mood Driven Computational Model for Gross Emotion Regulation Process Paradigm
Bibliographic record
Abstract
Judgments, preferences, and other cognitive tasks entail an emotional foundation and cannot function in an emotional vacuum. This essential emotional component how- ever, needs to be continuously monitored. Emotion regulation strategies target the potential risk of having inappropriate level of emotions in the process of decision making. This study is a follow-up on a formerly proposed computational model for emotion regulation strategies based on Gross theory and applies several enhancements to it. In particular, we extend the dynamism and realism of the original model by considering a dynamic environment in which we study the effect of emotion eliciting events such as psychiatric therapies or traumas occurring during the simulation period. Furthermore, the new model uses an emotion-dependent regulation process based on the mood of individuals. This approach is consistent with human behavior in the real life. In addition, some key pa- rameters in our proposed computational model, such as emotion persistence factor were made adaptive. Results obtained from the simulation experiments using our proposed model show further consistency with the base theory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".