Spatial frequencies affect cuteness perception of infant faces.
Bibliographic record
Abstract
Cuteness perception is a basic function in social interactions. Most studies focus on the impact of facial elemental features on cuteness ratings, but there are many factors that affect cuteness perception. Spatial frequency (SF) is one of the most important parameters in studies on faces. However, few studies have investigated the impact of SFs on cuteness perception. In this study, 16 images of infant faces with four cuteness levels were selected by a prerating experiment. Using a 7-point Likert scale paradigm, participants were asked to rate the cuteness of infant faces, including one version of broad unfiltered faces and four versions of filtered faces. The results showed that filtered SFs reduced cuteness ratings and that the impact of SFs was related to the cuteness levels of faces. Specifically, faces with low SFs got the lowest cuteness ratings. The ratings of faces with low SFs in neutral cuteness had a greater reduction than that in positive cuteness. In comparison, faces with medium and high SFs obtained relatively high cuteness ratings. However, the ratings in medium SFs were higher than that in high SFs if the cuteness of faces exceeded a certain level. Interestingly, their ratings reduction size increased with the improvement of cuteness levels. These results extend our understanding of the cuteness mechanism from an SF processing perspective. (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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".