Perceptual Learning of Inverted Faces across Different Spatial Frequency Bands
Bibliographic record
Abstract
Practicing a perceptual task can improve the ability to detect, discriminate, and identify stimuli, a phenomenon known as perceptual learning (1). Improvements have been found for simple and complex visual stimuli, including upright faces (2,3). However, little is known about the effects of practice on recognition of inverted faces (4,5). Here we hypothesized that adults might be better at learning to recognize inverted faces if trained with low spatial frequency images (<5 cpi; LSF) than full spectrum (FULL) or high spatial frequency images (>24 cpi; HSF) since newborns learn to recognize upright faces despite poor visual acuity (6). We trained 3 groups of 16 adults in a 10-AFC task with LSF, HSF or FULL inverted faces presented with different viewpoints. Participants were exposed either to the same or a different set of faces on Day 2. Although reaction times became significantly faster for all groups during training, accuracy improved only in the FULL (14%) and HSF (7%) groups. In contrast, the LSF group's overall improvement was only 1% (Fig1). Improvements from before to after training were assessed by comparisons to an untrained control group on 4 tasks. The FULL group showed more improvement in both accuracy and reactions time than controls on the delayed matching task of full spectrum inverted faces, thereby demonstrating generalization of training to novel faces. They were also faster on the same task with upright faces. The HSF group showed increased and decreased holistic processing of full spectrum inverted and upright faces, respectively, as indexed by reaction times on the composite face effect. Despite no improvement in accuracy during training, the LSF group improved more than controls on reaction times to match simultaneously presented full-spectrum inverted faces. These results suggest that perceptual learning of inverted faces and its generalization depend strongly on their spatial frequency content.
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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) | 0.003 | 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".