Rhythmic and interval‐based temporal orienting in autism
Bibliographic record
Abstract
Individuals with autism spectrum disorder (ASD) may show secondary sensory and cognitive characteristics, including differences in auditory processing, attention, and, according to a prominent hypothesis, the formulation and utilization of predictions. We explored the overlap of audition, attention, and prediction with an online auditory "temporal orienting" task in which participants utilized predictive timing cues (both rhythmic and interval-based) to improve their detection of faint sounds. We compared an autistic (n = 78) with a nonautistic (n = 83) group, controlling for nonverbal IQ, and used signal detection measures and reaction times to evaluate the effect of valid temporally predictive cues. We hypothesized that temporal orienting would be compromised in autism, but this was not supported by the data: the boost in performance induced by predictability was practically identical for the two groups, except for the small subset of the ASD group with co-occurring attention deficit hyperactivity disorder, who received less benefit from interval-based cueing. However, we found that the presence of a rhythm induced a significantly stronger bias toward reporting target detections in the ASD group at large, suggesting weakened response inhibition during rhythmic entrainment.
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 | 0.002 |
| 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) | 0.001 | 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".