Truth, control, and value motivations: the “what,†“how,†and “why†of approach and avoidance
Bibliographic record
Abstract
The hedonic principle-the desire to approach pleasure and avoid pain-is frequently presumed to be the fundamental principle upon which motivation is built. In the past few decades, researchers have enriched our understanding of how approaching pleasure and avoiding pain differ from each other. However, more recent empirical and theoretical work delineating the principles of motivation in humans and non-human animals has shown that not only can approach/avoidance motivations themselves be further distinguished into promotion approach/avoidance and prevention approach/avoidance, but that approaching pleasure and avoiding pain requires the functioning of additional distinct motivations-the motivation to establish what is real (truth) and the motivation to manage what happens (control). Considering these additional motivations in the context of moral psychology and animal welfare science suggests that these less-examined motives may themselves be fundamental to a comprehensive understanding of motivation, with major implications for the study of the "what," "how," and "why" of human and non-human approach and avoidance behavior.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".