The role of hedonics in the Human Affectome
Bibliographic record
Abstract
Experiencing pleasure and displeasure is a fundamental part of life. Hedonics guide behavior, affect decision-making, induce learning, and much more. As the positive and negative valence of feelings, hedonics are core processes that accompany emotion, motivation, and bodily states. Here, the affective neuroscience of pleasure and displeasure that has largely focused on the investigation of reward and pain processing, is reviewed. We describe the neurobiological systems of hedonics and factors that modulate hedonic experiences (e.g., cognition, learning, sensory input). Further, we review maladaptive and adaptive pleasure and displeasure functions in mental disorders and well-being, as well as the experience of aesthetics. As a centerpiece of the Human Affectome Project, language used to express pleasure and displeasure was also analyzed, and showed that most of these analyzed words overlap with expressions of emotions, actions, and bodily states. Our review shows that hedonics are typically investigated as processes that accompany other functions, but the mechanisms of hedonics (as core processes) have not been fully elucidated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".