The independent contributions of social reward and threat perceptions to romantic commitment.
Bibliographic record
Abstract
Although separate literatures have emerged on effects of social threats (i.e., rejection and negative evaluation) and rewards (i.e., connection and intimacy) on the process of commitment to a romantic relationship, no research has examined the influence of both simultaneously. Using an attachment framework, we examined the relation of social threats and rewards to investment model constructs (i.e., commitment, satisfaction, investment, quality of alternatives) in 3 studies. Study 1 (N = 533) and Study 2 (N = 866) assessed attachment styles, reward and threat perceptions, and investment model constructs, and data were analyzed using structural equation models. In Study 3 (N = 358), reward and threat perceptions were experimentally manipulated followed by measurement of investment model constructs. Results showed that attachment avoidance was uniquely associated with lower perceptions of reward, whereas attachment anxiety was uniquely associated with stronger perceptions of threat. Stronger reward perceptions were associated with higher commitment, investment, and satisfaction, as well as lower quality of alternatives in all studies. Stronger threat perceptions were associated with lower satisfaction in all 3 studies. Stronger threat perceptions were also correlated with higher levels of investment and commitment, although these effects did not replicate in our experimental study. Thus, perceptions of reward appear unambiguously associated with higher levels of all facets of commitment, whereas perceptions of threat are most strongly associated with lower satisfaction. These results underscore the importance of considering the effects of rewards and threats simultaneously in commitment processes.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".