Bibliographic record
Abstract
The emotion of pride appears to be a neurocognitive guidance system to capitalize on opportunities to become more highly valued and respected by others. Whereas the inputs and the outputs of pride are relatively well understood, little is known about how the pride system matches inputs to outputs. How does pride work? Here we evaluate the hypothesis that pride magnitude matches the various outputs it controls to the present activating conditions - the precise degree to which others would value the focal individual if the individual achieved a particular achievement. Operating in this manner would allow the pride system to balance the competing demands of effectiveness and economy, to avoid the dual costs of under-deploying and over-deploying its outputs. To test this hypothesis, we measured people's responses regarding each of 25 socially valued traits. We observed the predicted magnitude matchings. The intensities of the pride feeling and of various motivations of pride (communicating the achievement, demanding better treatment, investing in the valued trait and pursuing new challenges) vary in proportion: (a) to one another; and (b) to the degree to which audiences value each achievement. These patterns of magnitude matching were observed both within and between the USA and India. These findings suggest that pride works cost-effectively, promoting the pursuit of achievements and facilitating the gains from others' valuations that make those achievements worth pursuing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".