Local bias and local-to-global interference without global deficit: A robust finding in autism under various conditions of attention, exposure time, and visual angle
Bibliographic record
Abstract
A wide variety of paradigms have shown that autistic individuals present with superior performance on visual tasks. Here, the impact of task constraints on visual hierarchical processing in autism was investigated. By employing free- and forced-choice procedures, global and local processing of Navon-type hierarchical numerals was examined in 15 autistic persons (13 males, 2 females) and a comparison group. In the free-choice condition, autistics chose global and local targets randomly, though they were faster responding to local than to global targets, regardless of visual angle and exposure duration. In contrast, the comparison group exhibited a global advantage in naming time, which was evident only for shorter exposures, as well as effects of visual angle. In the forced-choice condition, autistics presented with a more important local-to-global interference than global-to-local interference, whereas the comparison group exhibited global advantage and bidirectional interference. Overall, the autistic participants presented with atypical local-to-global interference and local advantage in incongruent conditions (where global and local targets differ), in naming time as well as accuracy. The relative insensitivity of local bias to task constraints in autistics, in comparison to nonautistic participants, indicates that local bias, with local-to-global interference, is a key and characteristic feature of autistic visual cognition and a strong candidate for the "endophenotype" of autism.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".