Summarized Attribute Preferences Have Unique Antecedents and Consequences
Bibliographic record
Abstract
People have ideas about the attributes (i.e., traits or characteristics that vary along a dimension) that they like in others (e.g., “I like intelligence in a romantic partner”), and these ideas about liking are called summarized attribute preferences (Ledgerwood et al., 2018). But where do summarized preferences come from, and what do they predict? Across four studies, we examined how people form summarized attribute preferences and whether they predict situation selection. We showed participants a series of photographs of faces and assessed both their experienced liking for an attribute (or functional attribute preference) as well as their inference about how much they liked the attribute in the abstract (their summarized attribute preference). Our results suggest that summarized attribute preferences—despite being (weakly) grounded in functional attribute preferences—were affected by incidental aspects of the context in which people learn about them (i.e., the overall likeability of the pool of faces). Furthermore, we observed a double dissociation in the predictive validity of summarized and functional attribute preferences: Whereas summarized attribute preferences predicted situation selection at a distance (e.g., whether to join a new dating website based on a description of it), functional attribute preferences predicted situation selection with experience (e.g., whether to join a new dating website after sampling it). We discuss theoretical and methodological implications for the interdisciplinary science of human evaluation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".