Characterizing Attention Resource Capacity in Autism: A Multiple Object Tracking Study
Bibliographic record
Abstract
The extant literature aimed at characterizing attentional capability in autistics has presented inconsistent findings. This inconsistency and uncertainty may be the product of different theoretical and methodological approaches used to define attention in autism. In the current study, we investigate whether the allocation of attentional resources to task demands, and attention resource capacity, differs between autistics with no comorbid attention-deficit diagnosis (n = 55) and age-matched neurotypicals (n = 55). We compared differences in capacity and the allocation of resources by manipulating attentional load in a Multiple Object-Tracking (MOT) task, a robust, versatile, and ecological measure of selective, sustained, and distributed attention. While autistics demonstrated lower MOT performance, this difference disappeared when we accounted for fluid reasoning intelligence. Additionally, the similarity in the trend of MOT performance at increasing levels of attentional load between autistics and neurotypicals suggests no differences in the allocation of attentional resources to task demands. Taken together, our study suggests that higher-order cognitive abilities, such as intelligence, should be considered when characterizing attention across the autistic population in research. Similarly, our findings highlight the importance of considering cognitive competence when assessing attentional capabilities in autistic individuals, which could have significant implications for clinical diagnosis, treatment, and support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".