Signaling when (and when not) to be cautious and self-protective: Impulsive and reflective trust in close relationships.
Bibliographic record
Abstract
A dual process model is proposed to explain how automatic evaluative associations to the partner (i.e., impulsive trust) and deliberative expectations of partner caring (i.e., reflective trust) interact to govern self-protection in romantic relationships. Experimental and correlational studies of dating and marital relationships supported the model. Subliminally conditioning more positive evaluative associations to the partner increased confidence in the partner's caring, suggesting that trust has an impulsive basis. Being high on impulsive trust (i.e., more positive evaluative associations to the partner on the Implicit Association Test; Zayas & Shoda, 2005) also reduced the automatic inclination to distance in response to doubts about the partner's trustworthiness. It similarly reduced self-protective behavioral reactions to these reflective trust concerns. The studies further revealed that the effects of impulsive trust depend on working memory capacity: Being high on impulsive trust inoculated against reflective trust concerns for people low on working memory capacity.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".