The Effect of Early Visual Deprivation on the Development of Judgments of Attractiveness
Bibliographic record
Abstract
Adults find averaged faces that approximate the population mean to be more attractive than most other faces (Langlois & Roggman, 1990). By 3 months, infants appear to be able to form an average of faces they just saw (de Haan et al., 2001), but the influence of averageness on attractiveness judgments does not become as strong as in adults until after age 9 (Vingilis-Jaremko & Maurer, 2013). We investigated the importance of early visual experience by taking advantage of a rare condition: adults who were born with bilateral cataracts that blocked all patterned visual input until they were removed in the first year of life and the patient was given compensatory contact lenses. These adults experienced a period of visual deprivation during a sensitive period early in life for the development of many aspects of vision. We presented participants with pairs of faces that had been transformed toward and away from an average face of the same sex and asked them on each trial to select which face was more attractive. Averageness influenced the attractiveness judgments of adults treated for bilateral congenital cataracts, but to a lesser extent than in adults with normal vision. The data suggest that visual input early in life is necessary to set up the neural architecture that underlies the influence of averageness on adults' perception. That influence may be related to the establishment of a multi-dimensional 'face space' centred on a prototype face that is the mean of one's cumulative experience with faces (Valentine, 1991). The results are consistent with findings that cataract-reversal patients show less differentiation of upright and inverted faces (Le Grand et al., 2001) and have smaller-than-normal identity aftereffects. Together, the data suggest that early visual experience is necessary to form a normal face space centered on a veridical norm Meeting abstract presented at VSS 2014
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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 | 0.001 |
| 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) | 0.001 | 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".