What Pushes Your Buttons? How Knowledge about If-Then Personality Profiles Can Benefit Relationships
Bibliographic record
Abstract
Past research has debated the benefits of having accurate knowledge about a close other’s personality. However, this research has examined personality knowledge solely in terms of trait knowledge. We hypothesize that within close relationships, accuracy about personality profiles—a person’s “if-then” pattern of responses to situations—may often be more useful than accuracy about personality traits. We provide the first studies of if-then accuracy in close relationships, investigating trigger profiles, which describe a person’s unique pattern of reactivity to various potentially aversive interpersonal situations. For our studies, we first developed the Trigger Profile Questionnaire, consisting of 72 descriptions of potentially bothersome interpersonal behaviours. In Study 1 , friend-pairs rated how much each behaviour triggered them personally, and how much they thought it might trigger their friend. Defining accuracy as self-other agreement, findings demonstrated that having accurate knowledge about a friend’s trigger profile was associated with reduced feelings of relationship conflict for the friend, and increased feelings of depth and support for the self. Study 2 expanded this investigation to include behaviour adjustment as a potential moderator of this association. We predicted that accurate if-then knowledge would only be beneficial if participants used this knowledge to reduce engaging in behaviours that trigger the friend. Results from friend-pairs indicated that if-then accuracy was associated with feelings of depth and support in the relationship, as in Study 1. Participants’ if-then accuracy was not, however, associated with the friend's feelings of conflict. Moreover, there was almost no behaviour adjustment reported in the sample. Nevertheless, participants who did report adjusting their behaviour experienced less conflict in the relationship, as did their friends. No interactions between accuracy and adjustment were significant.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".