Expect and you shall perceive: People who expect better in turn perceive better behaviors from their romantic partners.
Bibliographic record
Abstract
People who are happy with their romantic relationships report that their partners are particularly effective at meeting their everyday relational needs. However, the literature invites competing predictions about how people arrive at those evaluations. In pilot research, we validated a scale of concrete, specific relationship behaviors that can be performed by a romantic partner day-to-day. In Study 1, cross-lagged panel models examined how expectations of positive behaviors, perceptions of positive behaviors, and relationship quality predict changes in one another from week to week. People who expected more positive behaviors in turn perceived more positive behaviors from their partners 1 week later. Key effects extended to negative relationship behaviors (Study 2). In Study 3, the same pattern emerged in a dyadic sample, with expected behaviors predicting changes in perceived behaviors independent of the partner's own reports. Truth and bias analyses revealed that people with lower expectations had more negatively biased perceptions of their partners' behaviors, whereas high expectations were associated with better accuracy. We obtained these results in the context of specific, verifiable behaviors reported on over relatively short periods, underscoring how powerfully people's everyday relationship perceptions may be shaped by their more global perceptions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.008 | 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".