Enhanced visual functioning in autism: An ALE meta‐analysis
Bibliographic record
Abstract
Autistics often exhibit enhanced perceptual abilities when engaged in visual search, visual discrimination, and embedded figure detection. In similar fashion, while performing a range of perceptual or cognitive tasks, autistics display stronger physiological engagement of the visual system than do non-autistics. To account for these findings, the Enhanced Perceptual Functioning Model proposes that enhanced autistic performance in basic perceptual tasks results from stronger engagement of sensory processing mechanisms, a situation that may facilitate an atypically prominent role for perceptual mechanisms in supporting cognition. Using quantitative meta-analysis of published functional imaging studies from which Activation Likelihood Estimation maps were computed, we asked whether autism is associated with enhanced task-related activity for a broad range of visual tasks. To determine whether atypical engagement of visual processing is a general or domain-specific phenomenon, we examined three different visual processing domains: faces, objects, and words. Overall, we observed more activity in autistics compared to non-autistics in temporal, occipital, and parietal regions. In contrast, autistics exhibited less activity in frontal cortex. The spatial distribution of the observed differential between-group patterns varied across processing domains. Autism may be characterized by enhanced functional resource allocation in regions associated with visual processing and expertise. Atypical adult organizational patterns may reflect underlying differences in developmental neural plasticity that can result in aspects of the autistic phenotype, including enhanced visual skills, atypical face processing, and hyperlexia.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".