Self-essentialist reasoning underlies the similarity-attraction effect.
Bibliographic record
Abstract
We propose that self-essentialist reasoning is a foundational mechanism of the similarity-attraction effect. Our argument is that similarity breeds attraction in two steps: (a) people categorize someone with a shared attribute as a person like me based on the self-essentialist belief that one's attributes are caused by an underlying essence and (b) then apply their essence (and the other attributes it causes) to the similar individual to infer agreement about the world in general (i.e., a generalized shared reality). We tested this model in four experimental studies (N = 2,290) using both individual difference and moderation-of-process approaches. We found that individual differences in self-essentialist beliefs amplified the effect of similarity on perceived generalized shared reality and attraction across both meaningful (Study 1) and minimal (Study 2) dimensions of similarity. We next found that manipulating (i.e., interrupting) the two crucial steps of the self-essentialist reasoning process-that is, by severing the connection between a similar attribute and one's essence (Study 3) and deterring people from applying their essence to form an impression of a similar other (Study 4)-attenuated the effect of similarity on attraction. We discuss the implications for research on the self, similarity-attraction, and intergroup phenomena. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".