A fuzzy computational model for emotion regulation based on Affect Control Theory
Bibliographic record
Abstract
In this article, we introduce a computational model for the appraisal processes underlying emotion regulation from the perspective of Affect Control Theory. According to this theory, the affective meaning of emotions, behaviours, objects, and other entities can be assessed and projected onto a three dimensional space of evaluation, potency, and activity. This concept was applied to events occurring in the environment of an affective agent in order to study the dynamics of emotional changes caused by these events. Several appraisal processes were used to effectively analyze the affective impact of the occurred events and to interpret them in terms of the three dimensions of the affect control theory. A fuzzy automata framework was investigated and found to be a good fit to represent the dynamics of changes in the affective states and to effectively picture the transitions between different emotional response levels. Based on the results obtained from conducted experiments, we can argue that the proposed model has the potential to be used in predicting the emotional changes in (human/virtual) agents as a result of the occurrence of emotion-triggering events. Furthermore, it would appear that the proposed model has the capability to be used as the kernel of an extended system developed for reverse engineering events, and to generate and apply those in-favor of emotion regulation process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".