Group Difference in Feature Scanning While Learning Novel Faces
Bibliographic record
Abstract
The scanpath used by typical individuals while looking at a familiar face is measurably different that used when they look at a novel face. It has been suggested that individuals with autism spectrum disorder (ASD) do not show such a difference. In this study we investigated the implicit timeline of novel faces becoming familiar. Twelve participants with high-functioning ASD or Asperger’s (Mean age = 28.08 years, SD= 6.29) and 16 controls (Mean age = 27.44, SD = 6.76) passively viewed 17 unique images of 6 individuals and 6 houses while eye gaze information was collection via eye tracking technology. Specifically we measured changes in the number of fixations and total fixation duration within two areas of interest (eyes and mouth for faces; upper and lower feature for houses). Both groups showed evidence of learning for both faces and houses; eye gaze patterns for both groups changed systematically with increased exposures. The effect of exposures was not significantly different between groups demonstrating that the process of learning novel faces and houses was similar in both groups. Analysis of mean number of fixations and total fixation duration per exposure revealed significant group differences: the ASD participants showed no differences in eye gaze patterns for the eyes and mouth areas of the face in both upright and inverted faces. However, the typical group showed a focus on eyes compared to the mouth and this difference was more evident for inverted faces. There were no group differences in the effects of time and mean gaze patterns for upright or inverted houses, indicating that group differences in learning complex stimuli are specific to social stimuli. The areas of the faces that individuals focus on differed between groups and this difference was even more evident for inverted faces, for which learning is a more complex social cognitive task. Meeting abstract presented at VSS 2012
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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 | 0.001 | 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".