OCC Model: Application and Comparison to the Dimensional Model of Emotion
Bibliographic record
Abstract
This paper presents a review and comparison between the model of cognitive structure of emotions (also known as OCC) and the dimensional circumplex of core affect for consumer products. The numbers of emotion types in each emotion group of OCC is compared and associated to those of circumplex of product emotion. Prospect-based group represents the highest number of emotions in the circumplex followed by well-being, Fortune-of-others, and well-being/attribution-compound group. Considering that the addressed circumplex originally targeted emotions generated by products' appearances and the prominent presence of prospect-based and well-being emotions on the circumplex, it is concluded that people judge the personal benefits of using products (consequences of events for self, in OCC terms) by their appearance. That is also confirmed by the eminent representation of attraction emotions on the circumplex, demonstrating the effect of visual aesthetics (as a product aspect) on attraction. Some of the differences between the two models were also established. It is asserted that OCC model uncovers the antecedents of emotions subscribing to the adapting function of emotions as a coping mechanism with the world while dimensional model is concerned with describing the nature of emotions and their dimensions. The significance of each model for the design practice is therefore determined by the design purpose in addressing emotions and the context of use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".