Dyadic Synchrony and Responsiveness Within the Context of Elevated Autism Likelihood: Applying Time-Varying Effect Models
Bibliographic record
Abstract
The ability to engage in synchronous interactions develops in the first year, as infants learn to sequentially regulate prosocial behaviors. Difficulty developing competence in these early social building blocks is linked to later developmental concerns, including autism spectrum disorder (ASD). Currently, metrics for quantifying social competence rely primarily on mean-level indices; however, interactions are dynamic. The present study modeled change in the odds of dyadic synchrony (DS), maternal responsiveness (MR), and infant responsiveness (IR) over time to explore if temporal patterns can inform developmental monitoring. Dyads were recruited from families with at least one older child with ASD (elevated ASD likelihood, n = 95) or families with no history of ASD (typical ASD likelihood, n = 72). Theory-driven indices of dyadic synchrony and responsiveness were derived from micro-analytically coded gaze, positive affect, and vocalizations. A series of logistic time-varying effect models (TVEMs) were conducted to compare temporal changes in synchrony and responsiveness across the infant/toddler groupings of (1) elevated- vs typical-ASD likelihood and (2) typical (TYP), ASD, or other developmental concerns (Non-ASD DC). DS, IR, and MR patterns were temporally stable but lower for the elevated ASD likelihood group. Temporal patterns of DS, IR, and MR were more variable for the ASD and Non-ASD DC groups. TVEMs captured meaningful dyadic information and could be used in future studies to inform prospective monitoring and parent-mediated intervention approaches.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".