More consistent, yet less sensitive: Interval timing in autism spectrum disorders
Bibliographic record
Abstract
Even though phenomenological observations and anecdotal reports suggest atypical time processing in individuals with an autism spectrum disorder (ASD), very few psychophysical studies have investigated interval timing, and the obtained results are contradictory. The present study aimed to clarify which timing processes function atypically in ASD and whether they are related to the ASD diagnostic profile. Visual, auditory, and cross-modal interval timing was assessed in 18 individuals with ASD using a repeated standards version of the temporal generalization task. The use of two different standard durations (600 and 1,000 ms) allowed for an assessment of the scalar property of interval timing in ASD, a fundamental characteristic of interval timing. The ASD group showed clearer adherence to the scalar property of interval timing than the control group. In addition, both groups showed the normal effect that auditory stimuli had longer subjective durations than visual ones. Yet, signal detection analysis showed that the sensitivity of temporal discrimination was reduced in the ASD group across modalities, in particular for auditory standards. Moreover, response criteria in the ASD group were related to symptom strength in the communication domain. The findings suggest that temporal intervals are fundamentally processed in the same way in ASD and TD, but with reduced sensitivity for temporal interval differences in ASD. Individuals with ASD may show a more conservative response strategy due to generally decreased sensitivity for the perception of time intervals.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".