A Fuzzy Logic Computational Model for Emotion Regulation Based on Gross
Why this work is in the frame
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Bibliographic record
Abstract
Emotion regulation looks into methods and strategiesthat humans use in order to control and balance theirpossible extreme levels of emotions. One importantchallenge in building a computational model of emotionsis the mainly non-quantitative nature of this problem.In this paper, we investigate a Fuzzy logic approachas a possible framework for providing the requiredqualitative and quantitative description of suchmodels. In our proposed fuzzy computational modelwhich was constructed based on Gross theory for emotionregulation, beside the fuzzy structure, it includesa learning module that enhances the model adaptivityto environmental changes through learning some relevantaspects such as patterns of events’ sequences. Theresults of the simulation experiments were comparedagainst a formerly presented non-fuzzy implementation.We observed that the agents in our proposed modelmanaged to cope better with changes in the environmentand exhibited smoother regulation behavior. Moreover,our model showed further consistency with the inferentialrules of Gross theory.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it