Unveiling Mental Self-Images from Face Perception and Memory
Bibliographic record
Abstract
Human cognition and behavior are intricately shaped by self-perception, self-concept and self-esteem. While prior work has begun to uncover visual traits of self-representations, detailed depictions of mental self-images and their evaluation with respect to systematic biases are still largely missing from the field. To address this, our study aims to uncover self-images from perception and memory, as well as to assess their sensitivity to specific perceptual abilities and personality traits. To this end, female Caucasian adults (N=30) evaluated the visual similarity between pairs of female face stimuli, including images of their own faces, as well as between mental images of themselves, as recalled from memory, and other face stimuli. Perception and memory-based self-images were then derived through behavior-based image reconstruction as applied to similarity data and assessed with respect to their visual content. Our investigation revealed significant levels of reconstruction accuracy relative to actual face images of the participants. It further revealed systematic correspondence between perception and memory-based representations of the self. Further, it demonstrated the impact of specific factors (e.g., as captured by self-attractiveness ratings) on the content of self-images. Thus, our findings shed new light on self-representations, on their visual content and on the factors that impact their veracity.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".