Infant Neural Sensitivity to Dynamic Eye Gaze Is Associated with Later Emerging Autism
Bibliographic record
Abstract
Autism spectrum disorders (henceforth autism) are diagnosed in around 1% of the population [1]. Familial liability confers risk for a broad spectrum of difficulties including the broader autism phenotype (BAP) [2, 3]. There are currently no reliable predictors of autism in infancy, but characteristic behaviors emerge during the second year, enabling diagnosis after this age [4, 5]. Because indicators of brain functioning may be sensitive predictors, and atypical eye contact is characteristic of the syndrome [6-9] and the BAP [10, 11], we examined whether neural sensitivity to eye gaze during infancy is associated with later autism outcomes [12, 13]. We undertook a prospective longitudinal study of infants with and without familial risk for autism. At 6-10 months, we recorded infants' event-related potentials (ERPs) in response to viewing faces with eye gaze directed toward versus away from the infant [14]. Longitudinal analyses showed that characteristics of ERP components evoked in response to dynamic eye gaze shifts during infancy were associated with autism diagnosed at 36 months. ERP responses to eye gaze may help characterize developmental processes that lead to later emerging autism. Findings also elucidate the mechanisms driving the development of the social brain in infancy.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".