Experience shapes the granularity of social perception: Computational insights into individual and group-based representations.
Bibliographic record
Abstract
People are regularly conceptualized at varying levels of resolution, sometimes characterized by their idiosyncratic features while at other times seen as mere tokens of their social groups. Decades of research have sought to understand when perceivers will draw upon each of these types of representations, detailing the perceiver- and target-related features that may decrease reliance on stereotypes in favor of individuated knowledge. However, little work has examined how these representations might be formed in the first place: In order for individuated representations of others to be used, they must first be built through experience. Here, we offer a novel approach to characterizing the formation of social representations through the use of computational models of category learning. Across three experiments, participants learned about members of novel social groups who behaved positively or negatively toward them. Computational modeling of participants' task behavior revealed a critical interaction of perceiver motivations and learning context on representations. Participants who received selective feedback about targets only upon approaching them formed more categorical representations than those who received full feedback. Further, we found tentative evidence that this difference was most pronounced in those who held more racist attitudes, measured in an entirely separate context. Thus, more informative learning contexts could potentially act as a "protective factor" that shields perceivers' representations from their negative attitudes. The results shed light on the psychological underpinnings of prejudice, using a novel approach to reveal how social categorization is selectively employed in a manner that maintains negative stereotypes. (PsycInfo Database Record (c) 2025 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".