Fuzzy Causal Patterns of Humor and Jokes for Cognitive and Affective Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humor is an advanced emotional and cognitive ability of mankind that involves complex semantic inference and deep passionate appreciation. This paper presents the cognitive foundations of amusement and a general theory of humor based on the recent advances in cognitive informatics, cognitive linguistics, cognitive computing, and fuzzy causal analyses. A theory of fuzzy false causation (FFC) is introduced that reveals humor and jokes as false causations in fuzzy causal inferences. Base on the FFC theory, a general pattern of humor (GPH) is formalized for analyzing the settings and appreciations of a set of sample jokes. A formal measurement of the degree of amusement in jokes and humor is quantitatively described towards the rational explanation of jokes based on cognitive affective assessment. The formal models of humor and jokes enable machines for humor comprehension and appreciation in artificial intelligence, cognitive computing, computational intelligence, and cognitive robots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it