Abnormality in face scanning by children with Autism Spectrum Disorder is limited to the eye region: Evidence from multi-method analyses of eye tracking
Bibliographic record
Abstract
There has been considerable controversy regarding whether children with autism spectrum disorder (ASD) and typically developing children (TD) show different eye movement patterns when processing faces. We investigated ASD and age- and IQ-matched TD children’s fixations and scanning of faces using a novel multi-method approach inclusive of the traditional AOI approach, a data-driven analysis, and a saccade path analysis. We found that ASD children spent less time looking at the whole face generally. After controlling for this difference, ASD children’s fixations of the other face parts, except for the eye region, and their scanning paths between face parts were comparable either to the age-matched or IQ-matched TD groups. In contrast, in the eye region, ASD children’s scanning differed significantly from that of both TD groups in a highly specific manner. First, ASD children fixated significantly less on the right eye than both TD groups. Second, unlike both TD groups, ASD children’s fixations were more biased towards the left eye region. Third, their fixations to the left eye region were different from those of both TD groups: Whereas TD children fixated on the pupil region of the eye, ASD children fixated below the left eye as though they were trying to avoid direct eye contact. We conclude that ASD children do not have a general and pervasive abnormality in face scanning. Rather, their abnormality is limited only to the eye region, likely due to their strong tendency to avoid eye contact. This abnormality may be partially responsible for ASD children’s significantly poorer face recognition performance than TD children. Meeting abstract presented at VSS 2013
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".