Instrumentalizing Cognitive Dissonance Emotions
Bibliographic record
Abstract
Many psychologists think that there are few basic emotions, and most emotions are combinations of these few. Here we advance a hypothesis that the number of principally different emotions is near infinite. We consider emotions as mental states with hedonic content, indicating satisfaction and dissatisfaction. Basic emotions correspond to bodily signals, and there are relatively few of them. Our hypothesis is that a large number of emotions are related to the knowledge instinct (KI, or a need for knowledge). KI drives the mind to fit mental representations to cognitive experiences and to resolve mental contradictions. Discomfort due to holding contradictory knowledge elements are known as cognitive dissonances. We emphasize that cognitive dissonances involve specific emotions. The number of cognitive dissonances is combinatorial in terms of elements of knowledge. Correspondingly, the number of these knowledge-related emotions is very large. We report experimental results on measuring these emotions and indicating that emotions of cognitive dissonance exist. We also make a step toward proving that these emotions are different from basic emotions in principle, and outline future research directions toward proving that their number is large.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".