The unique contributions of perceiver and target characteristics in person perception.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Models of person perception have long asserted that our impressions of others are guided by characteristics of both the target and perceiver. However, research has not yet quantified to what extent perceivers and targets contribute to different impressions. This quantification is theoretically critical, as it addresses how much an impression arises from "our minds" versus "others' faces." Here, we apply cross-classified random effects models to address this fundamental question in social cognition, using approximately 700,000 ratings of faces. With this approach, we demonstrate that (a) different trait impressions have unique causal processes, meaning that some impressions are largely informed by perceiver-level characteristics whereas others are driven more by physical target-level characteristics; (b) modeling of perceiver- and target-variance in impressions informs fundamental models of social perception; (c) Perceiver × Target interactions explain a substantial portion of variance in impressions; (d) greater emotional intensity in stimuli decreases the influence of the perceiver; and (e) more variable, naturalistic stimuli increases variation across perceivers. Important overarching patterns emerged. Broadly, traits and dimensions representing inferences of character (e.g., dominance) are driven more by perceiver characteristics than those representing appearance-based appraisals (e.g., youthful-attractiveness). Moreover, inferences made of more ambiguous traits (e.g., creative) or displays (e.g., faces with less extreme emotions, less-controlled stimuli) are similarly driven more by perceiver than target characteristics. Together, results highlight the large role that perceiver and target variability play in trait impressions, and develop a new topography of trait impressions that considers the source of the impression. (PsycINFO Database Record
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it